Search

This paper presents a new objective quality metric that was adapted to the complex characteristics of immersive video (IV) which is prone to errors caused by processing and compression of multiple input views and virtual view synthesis. The proposed metric, IV-PSNR, contains two techniques that allow for the evaluation of quality loss for typical immersive video distortions: corresponding pixel shift and global component difference. The performed experiments compared the proposal with 31 state-of-the-art quality metrics, showing their performance in the assessment of quality in immersive video coding and processing, and in other applications, using commonly used image quality assessment databases– TID2013 and CVIQ. As presented, IV-PSNR outperforms other metrics in immersive video applications and still can be efficiently used in the evaluation of different images and videos. Moreover, basing the metric on the calculation of PSNR allowed the computational complexity to remain low. Publicly available, efficient implementation of IV-PSNR software was provided by the authors of this paper and is used by ISO/IEC MPEG for evaluation and research on the upcoming MPEG Immersive video (MIV) coding standard. [ABSTRACT FROM AUTHOR]

Recently, research on six-degree-of-freedom virtual reality (VR) systems based on a light field (LF) has been actively conducted. The LF-based approach is photorealistic and less prone to errors than the existing three-dimensional (3D) modeling-based approach. On the other hand, because the amount of data is very large for LF-based immersive virtual reality, it is important to manage the data efficiently. This paper makes two major contributions to data management for rendering acceleration in this context. First, the GPUs commonly used for high-speed rendering require optimization of the data transfer process between the host memory and the device memory. This paper proposes a simple but effective way to maximize data reuse by finding the proper size of a transfer unit depending on the target applications and the system capability constraints. Also, view synthesis and data transfer are performed in a pipelined manner to hide the data movement time. Second, for the LF rendering of a wide space, data management in the storage-host-GPU hierarchy is attempted for the first time. As the size of the virtual space increases, the required amount of LF data grows rapidly and exceeds the allocatable host memory capacity. Because the loading of data stored in storage is a very slow process, a system-level LF rendering structure that carefully considers the characteristics of the storage-host-GPU memory hierarchy should be designed. In this paper, progressive LF updating is proposed, and it prevents rendering stalls caused by slow storage data loading. Experimental results show that a 360-degree view with a $9000 \times 2048$ resolution can be rendered in an average of 2.57 ms from LF data with a $4096 \times 2048$ resolution, achieving performance close to 400 frames per second. [ABSTRACT FROM AUTHOR]

Video saliency prediction (VSP) aims to imitate eye fixations of humans. However, the potential of this task has not been fully exploited since existing VSP methods only focus on modeling visual saliency of the input previous frames. In this paper, we present the first attempt to extend this task to video saliency forecasting (VSF) by forecasting attention regions of consecutive future frames. To tackle this problem, we propose a video saliency forecasting transformer (VSFT) network built on a new encoder-decoder architecture. Different from existing VSP methods, our VSFT is the first pure-transformer based architecture in the VSP field and is freed from the dependency of the pretrained S3D model. In VSFT, the attention mechanism is exploited to capture spatial-temporal dependencies between the input past frames and the target future frame. We propose cross-attention guidance blocks (CAGB) to aggregate multi-level representation features to provide sufficient guidance for forecasting. We conduct comprehensive experiments on two benchmark datasets, DHF1K and Hollywoods-2. We investigate the saliency forecasting and predicting abilities of existing VSP methods by modifying the supervision signals. Experimental results demonstrate that our method achieves superior performance on both VSF and VSP tasks. [ABSTRACT FROM AUTHOR]

3D human trajectory reconstruction in an indoor scene is critical in various applications, such as indoor navigation and human activity recognition. This task is challenging due to occlusion and clutters of indoor scenes, flexible human body joints, and severe lack of relevant datasets. Although several methods have been proposed to reconstruct a 3D human trajectory, they either can recover only 2D positions or require human initiative cooperation. In this paper, we propose a novel framework for 3D human trajectory reconstruction in an indoor scene using monocular surveillance videos and static point clouds without any initiative cooperation. The proposed framework consists of three modules: 3D pose estimation, depth regression, and trajectory reconstruction. We first estimate 3D pose from videos. Especially, we reconstruct a half-body 3D pose to deal with the occlusion problem. Then, we propose a depth regression approach to iteratively regress the depth of a 3D pose. Unlike data-driven approaches, our depth regression approach does not require training data and can be integrated into any 3D pose model. Finally, we exploit the geometric constraints from the point cloud to optimize the 3D trajectory. We evaluated the 3D pose estimation and depth regression modules on the H3.6M datasets. Due to the lack of evaluation datasets, we also built a trajectory dataset to evaluate the trajectory reconstruction performance. Empirical evaluation shows that our framework achieves accurate trajectory reconstruction results on real-world videos. [ABSTRACT FROM AUTHOR]

Passive video forensics has become an active topic in recent years. Generally, a pristine video obtained from surveillance cameras or other video recording devices is single compressed. At the same time, double lossy compression will certainly be introduced in tampered videos since it needs to go through recompression to perform tampering on a commonly used compressed video. In the video’s double compression, some traces are left due to the intrinsic effects of recompression. Traditional supervised learning is inefficient because it requires two classes (the pristine and the manipulated) that occur in the video to be exhaustively assigned labels. Actually, compared with pristine videos, manipulated videos with labels are more difficult to obtain. To address this problem, in this paper, one-class classification, which is often used for anomaly detection and only needs the target class, is introduced. We first treat all decompressed video frames as still images and extract subtractive pixel adjacency matrix (SPAM) steganalysis features to detect traces left in the double compression process. Then we adopt a Gaussian density-based one-class classifier since SPAM features extracted from pristine video frames approximately subject to the Gaussian distribution. Furthermore, we improve the robustness of the classifier by using ensemble strategy. Experimental results indicate that our proposed method exceeds other more complex one-class classification methods, and outperforms fully-supervised learning methods only by feeding features from single compressed video frames to our one-class classifier. [ABSTRACT FROM AUTHOR]

Video summarization is an economical way of representing video contents and is useful for effective and quick browsing of the relevant activity present in the video. Most current video summarization approaches do not provide the correct insight of the events occurring in the video, such as moving the objects in a nonchronological order and tampering with the background and size of the objects. In this paper, we present an approach to create a video summarization that is a precise representation of the video content. First, our approach finds out the salient activities that are taking place in the video using an optimization framework for static and dynamic scenes. Second, the frames with the salient activities are stitched using alpha matting to form a single frame. Third, the summarized frame over multiple video shots obtained by our approach gives superior retrieval performance with image queries while reducing retrieval latency and memory requirement. This paper proposes a single frame-based indexing of the video database instead of multi-frame indexing as it is an interesting and potentially promising approach. Finally, several experiments are carried out to evaluate the proposed approach. The experiments demonstrate that the video summarization produced by our approach gives a realistic view of the events in the video, while the reduction in computational complexity and memory requirement is high. [ABSTRACT FROM AUTHOR]

This paper considers stabilizing traffic videos, which are recorded by cameras mounted on moving vehicles. Compared with videos captured by hand-held cameras, traffic videos are more difficult to stabilize due to dynamic scenes, higher frequency camera jitter, more moving foreground objects and more serious parallax. The conventional video stabilization methods usually estimate the camera jitter from the background feature trajectories which are mainly determined by the camera motion, and then stabilize videos with the estimated jitter. These methods have to correctly distinguish background and foreground feature trajectories, which is not trivial, and may suffer from performance degradation when large foreground objects exist and no enough number of background feature trajectories can be obtained. To resolve these issues, this paper proposes a novel stabilization method, under which background and foreground feature trajectories are no longer distinguished and work together to yield stabilized trajectories. More specifically, the movement of all feature trajectories is modeled as the summation of the camera motion and the object motion. By solving an optimization problem, we can remove the high frequency components of the camera motion, i.e., the camera jitter, and stabilize videos. Parallax is also treated as object motion and contributes feature trajectories for video stabilization. As our method makes use of both foreground and background feature trajectories, it can outperform the conventional stabilization methods which use only background feature trajectories, especially when there are large foreground objects and the number of extracted background feature trajectories is small. Furthermore, some refinements are proposed to speed up our method and enhance its robustness. Experiments are done and confirm its performance superiority. [ABSTRACT FROM AUTHOR]

Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming. However, higher-order relationships across the entire camera network are ignored by these methods, leading to contradictory outputs when matching results from different camera pairs are combined. In this paper, we address the problem of unsupervised video-based re-identification by proposing a consistent cross-view matching (CCM) framework, in which global camera network constraints are exploited to guarantee the matched pairs are with consistency. Specifically, we first propose to utilize the first neighbor of each sample to discover relations among samples and find the groups in each camera. Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network. Finally, we learn metric models for camera pairs progressively by alternatively mining consistent cross-view matching pairs and updating metric models using these obtained matches. Rigorous experiments on two widely-used benchmarks for video re-identification demonstrate the superiority of the proposed method over current state-of-the-art unsupervised methods; for example, on the MARS dataset, our method achieves an improvement of 4.2% over unsupervised methods, and even 2.5% over one-shot supervision-based methods for rank-1 accuracy. [ABSTRACT FROM AUTHOR]

Video stabilization is essential for improving the visual quality of shaky videos. Current video stabilization methods usually take feature trajectories in the background to estimate one global transformation matrix or several transformation matrices based on a fixed mesh, and warp shaky frames into their stabilized views. However, these methods may not model the shaky camera motion well in complicated scenes, such as scenes containing large foreground objects or strong parallax, and may result in notable visual artifacts in the stabilized videos. To resolve the above issues, this paper proposes an adaptively meshed method to stabilize a shaky video based on all of its feature trajectories and an adaptive blocking strategy. More specifically, we first extract the feature trajectories of the shaky video and then generate a triangle mesh according to the distribution of the feature trajectories in each frame. Then, the transformations between shaky frames and their stabilized views over all triangular grids of the mesh are calculated to stabilize the shaky video. Since more feature trajectories can usually be extracted from all of the regions, including both the background and foreground regions, a finer mesh will be obtained and provided for camera motion estimation and frame warping. We estimate the mesh-based transformations of each frame by solving a two-stage optimization problem. Moreover, foreground and background feature trajectories are no longer distinguished and both contribute to the estimation of the camera motion in the proposed optimization problem, yielding better estimation performance than previous works, particularly in challenging videos with large foreground objects or strong parallax. To further enhance the robustness of our method, we propose two adaptive weighting mechanisms to improve its spatial and temporal adaptability. Experimental results demonstrate the effectiveness of our method in producing visually pleasing stabilization effects in various challenging videos. [ABSTRACT FROM AUTHOR]

Omnidirectional (or 360°) images and videos are emergent signals being used in many areas, such as robotics and virtual/augmented reality. In particular, for virtual reality applications, they allow an immersive experience in which the user can interactively navigate through a scene with three degrees of freedom, wearing a head-mounted display. Current approaches for capturing, processing, delivering, and displaying 360° content, however, present many open technical challenges and introduce several types of distortions in the visual signal. Some of the distortions are specific to the nature of 360° images and often differ from those encountered in classical visual communication frameworks. This paper provides a first comprehensive review of the most common visual distortions that alter 360° signals going through the different processing elements of the visual communication pipeline. While their impact on viewers’ visual perception and the immersive experience at large is still unknown—thus, it is an open research topic—this review serves the purpose of proposing a taxonomy of the visual distortions that can be encountered in 360° signals. Their underlying causes in the end-to-end 360° content distribution pipeline are identified. This taxonomy is essential as a basis for comparing different processing techniques, such as visual enhancement, encoding, and streaming strategies, and allowing the effective design of new algorithms and applications. It is also a useful resource for the design of psycho-visual studies aiming to characterize human perception of 360° content in interactive and immersive applications. [ABSTRACT FROM AUTHOR]

In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collective motion. In broadcast basketball videos, specific camera motions are used to present specific events. Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion. A semantic event in basketball videos can be generally divided into three stages: pre-event, event occurrence (event-occ), and post-event. By analyzing the influence of different stages of video segments to semantic events discrimination, it is observed that the pre-event and event-occ segments are effective for classification, while the post-events are effective for event success/failure classification. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for the basketball event classification. First, a two-stage GCMP-based event classification scheme is proposed. The GCMP is extracted using the optical flow. The two-stage scheme progressively combines a five-class event classification algorithm on event-occs and a two-class event classification algorithm on pre-events. Both algorithms utilize the sequential convolutional neural networks (CNNs) and the long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize the post-event segments to predict success/failure using deep features of images in the video frames (RGB_DF_VF)-based algorithms. Finally, the event classification results and success/failure classification results are integrated to obtain the final results. To evaluate the proposed scheme, we collected a new dataset called NCAA+, which is automatically obtained from the NCAA dataset by extending the fixed length of video clips forward and backward of the corresponding semantic events. The experimental results demonstrate that the proposed scheme achieves the mean average precision of 58.10% on NCAA+. It is higher by 6.50% than the state of the art on NCAA. [ABSTRACT FROM AUTHOR]

By providing angular and spatial information of light rays, light field images are widely used in many applications. To capture large field of view light field, existing approaches either stitch small field of view light fields, whose apertures are also small, or leverage specialized equipments, which are not accessible to ordinary users. In this paper, we present a method to extract fully 360° field of view panoramic light fields from densely sampled hand-held videos. Unlike previous works, our method handles the large exposure variation and motion blur problems, which are common in the panoramic capture and hand-held videos. To provide real-time rendering performance, our method applies light field sampling to the extracted panoramic light field. Based on the unstructured representation, our method can sample the light field without explicit parameterization. We formulate the sampling problem into a set multicover problem and solve it globally using integer linear programming. Compared with the greedy based method, our approach can use much less video frames to represent the panoramic light field, but still can get better rendering results. [ABSTRACT FROM AUTHOR]

This paper presents an effective two-stream convolutional neural network (CNN)-based approach to detect multiple spatial-temporal action tubes in videos. A novel video localization refinement (VLR) scheme is first addressed to iteratively rectify the potentially inaccurate bounding boxes by exploiting the temporal consistency between adjacent frames. Then, to provide more faithful detection scores, a new fusion strategy is considered, which combines not only the appearance and the flow information of the two-stream networks but also the motion saliency, the latter of which is included to address the small camera motion. In addition, an efficient multiple path search (MPS) algorithm is developed to simultaneously identify multiple paths in a single run. In the forward message passing of MPS, each node stores information of a prescribed number of connections based on the accumulated scores determined in the previous stages. A backward path tracing is invoked afterward to find all multiple paths at the same time by fully reusing the information generated in the forward pass without repeating the search process. Thus, the complexity incurred can be reduced. The simulation results show that, together with VLR and the new fusion scheme, the proposed MPS, in general, can provide superior performance compared with the state-of-the-art works on four public datasets. [ABSTRACT FROM AUTHOR]

Both a high spatial and a high temporal resolution of images and videos are desirable in many applications, such as entertainment systems, monitoring manufacturing processes, or video surveillance. Due to the limited throughput of pixels per second, however, there is always a tradeoff between acquiring sequences with a high spatial resolution at a low temporal resolution or vice versa. In this paper, a modified sensor concept is proposed which is able to acquire both a high spatial and a high temporal resolution. This is achieved by dynamically reading out only a subset of pixels in a non-regular order to obtain a high temporal resolution. A full high spatial resolution is then obtained by performing a subsequent 3D reconstruction of the partially acquired frames. The main benefit of the proposed dynamic readout is that for each frame, different sampling points are available, which is advantageous since this information can significantly enhance the reconstruction quality of the proposed reconstruction algorithm. Using the proposed dynamic readout strategy, gains in the peak-signal-to-noise ratio (PSNR) of up to 8.55 dB are achieved compared with a static readout strategy. Compared with the other state-of-the-art techniques, such as frame rate up-conversion or super-resolution, which are also able to reconstruct sequences with both a high spatial and a high temporal resolution, average gains in PSNR of up to 6.58 dB are possible. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of RGB-D egocentric action recognition. Unlike conventional human action videos that are passively recorded by static cameras, egocentric videos are self-generated from wearable sensors that are more flexible and provide the close-ups with the visual attention of the wearers when they act. Moreover, RGB-D videos contain the spatial appearance and temporal information in the RGB modality and reflect the 3D structure of the scenes in the depth modality. To adequately learn the nonlinear structure of heterogeneous representations from different modalities and exploit their complementary characteristics, we develop a multi-stream deep neural networks (MDNN) method, which aims to preserve the distinctive property for each modality and simultaneously explore their sharable information in a unified deep architecture. Specifically, we deploy a Cauchy estimator to maximize the correlations of the sharable components and enforce the orthogonality constraints on the distinctive components to guarantee their high independencies. Since the egocentric action recognition is usually sensitive to hand poses, we extend our MDNN by integrating with the hand cues to enhance the recognition accuracy. Extensive experimental results on a newly collected data set and two additional benchmarks are presented to demonstrate the effectiveness of our proposed method for RGB-D egocentric action recognition. [ABSTRACT FROM AUTHOR]

Siamese networks are prevalent in person re-identification (re-id) tasks to address the similarity and dissimilarity among video frames. It mainly focuses on the inter-video variation between spatio-temporal features extracted from different videos, while the variation between features of the same video has been rarely discussed. In this paper, we introduce the concept of “mean-body” and define an intra-video loss to address the variation between spatio-temporal features of the same video. A novel loss is presented to boost the training of the re-id networks by combining the proposed intra-video loss and the Siamese loss. Specifically, the intra-video loss uses the unique mean-body of each camera viewpoint to make the video sequence more clustered, while the Siamese loss is to make the wrong matching videos more separated. To train the whole network, we update the network and the mean-body in an iterative manner. As a result, the proposed loss is expected to improve the generalization capability of the re-id networks on the testing set. Extensive results demonstrate that the presented approach outperforms the state-of-the-art algorithms on the publicly available data sets, such as PRID2011, iLIDS-VID, and MARS, in terms of re-id accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a video-based person re-identification system for wide area tracking based on a recurrent neural network architecture. Given short video sequences of a person, generated by a tracking algorithm, our video re-identification algorithm links these tracklets in full trajectories across a network of non-overlapping cameras in an open-world scenario. In our system, features are first extracted from each frame using a convolutional neural network. Then, a recurrent layer combines information across time-steps. The features from all time-steps are finally combined using temporal pooling to give an overall appearance feature for the complete sequence. Our system is trained to perform re-identification using a Siamese network architecture. Experiments are conducted on the iLIDS-VID and PRID-2011 video re-identification data sets as well as in the DukeMTMC multi-camera tracking data set. [ABSTRACT FROM AUTHOR]

Surveying wide areas with only one camera is a typical scenario in surveillance and automotive applications. Ultra wide-angle fisheye cameras employed to that end produce video data with characteristics that differ significantly from conventional rectilinear imagery as obtained by perspective pinhole cameras. Those characteristics are not considered in typical image and video processing algorithms such as motion estimation, where translation is assumed to be the predominant kind of motion. This contribution introduces an adapted technique for use in block-based motion estimation that takes into the account the projection function of fisheye cameras and thus compensates for the non-perspective properties of fisheye videos. By including suitable projections, the translational motion model that would otherwise only hold for perspective material is exploited, leading to improved motion estimation results without altering the source material. In addition, we discuss extensions that allow for a better prediction of the peripheral image areas, where motion estimation falters due to spatial constraints, and further include calibration information to account for lens properties deviating from the theoretical function. Simulations and experiments are conducted on synthetic as well as real-world fisheye video sequences that are part of a data set created in the context of this paper. Average synthetic and real-world gains of 1.45 and 1.51 dB in luminance PSNR are achieved compared against conventional block matching. Furthermore, the proposed fisheye motion estimation method is successfully applied to motion compensated temporal resolution enhancement, where average gains amount to 0.79 and 0.76 dB. [ABSTRACT FROM AUTHOR]

Recognizing human actions from varied views is challenging due to huge appearance variations in different views. The key to this problem is to learn discriminant view-invariant representations generalizing well across views. In this paper, we address this problem by learning view-invariant representations hierarchically using a novel method, referred to as joint sparse representation and distribution adaptation. To obtain robust and informative feature representations, we first incorporate a sample-affinity matrix into the marginalized Stacked Denoising Autoencoder to obtain shared features that are then combined with the private features. In order to make the feature representations of videos across views transferable, we then learn a transferable dictionary pair simultaneously from pairs of videos taken at different views to encourage each action video across views to have the same sparse representation. However, the distribution difference across views still exists because a unified subspace, where the sparse representations of one action across views are the same, may not exist when the view difference is large. Therefore, we propose a novel unsupervised distribution adaptation method that learns a set of projections that project the source and target views data into respective low-dimensional subspaces, where the marginal and conditional distribution differences are reduced simultaneously. Therefore, the finally learned feature representation is view-invariant and robust for substantial distribution difference across views even though the view difference is large. Experimental results on four multi-view datasets show that our approach outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, a system for tactic analysis of fencing video is developed. The proposed system uses three novel views for presenting the tactic and critical distance of fencers in a match point-by-point. Since fencers fight each other directly, identifying the moment of launching an attack is crucial for analyzing fencing tactics. A criterion function in terms of the speed and acceleration of a fencer and the tempo of the fencer’s footwork is proposed for identifying that moment. In addition, a rule-based approach based on the fencer speed and distance is proposed for recognizing the offensive tactic used to score a hit. Three experiments on the video clips for finals and semi-finals of individual events of the World Fencing Championships in 2013–2015 were conducted for evaluating the proposed system. The experiment also included objective and subjective assessments of twelve fencers. The experimental results have demonstrated the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Video summarization is the process to extract the most significant contents of a video and to represent it in a concise form. The existing methods for video summarization could not achieve a satisfactory result for a video with camera movement and significant illumination changes. To solve these problems, in this paper, a new framework for video summarization is proposed based on eye tracker data, as human eyes can track moving object accurately in these cases. The smooth pursuit is the state of eye movement when a user follows a moving object in a video. This motivates us to implement a new method to distinguish smooth pursuit from other type of gaze points, such as fixation and saccade. The smooth pursuit provides only the location of moving objects in a video frame; however, it does not indicate whether the located moving objects are very attractive (i.e., salient regions) to viewers or not, as well as the amount of motion of the moving objects. The amount of salient regions and object motions are the two important features to measure the viewer’s attention level for determining the key frames for video summarization. To find the most attractive objects, a new spatial saliency prediction method is also proposed by constructing a saliency map around each smooth pursuit gaze point based on human visual field, such as fovea, parafoveal, and perifovea regions. To identify the amount of object motions, the total distances between the current and the previous gaze points of viewers during smooth pursuit are measured as a motion saliency score. The motivation is that the movement of eye gaze is related to the motion of the objects during smooth pursuit. Finally, both spatial and motion saliency maps are combined to obtain an aggregated saliency score for each frame and a set of key frames are selected based on user selected or system default skimming ratio. The proposed method is implemented on Office video data set that contains videos with camera movements and illumination changes. Experimental results confirm the superior performance of the proposed spatial and motion saliency prediction method compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In computer vision, tracking humans across camera views remain challenging, especially for complex scenarios with frequent occlusions, significant lighting changes, and other difficulties. Under such conditions, most existing appearance and geometric cues are not reliable enough to distinguish humans across camera views. To address these challenges, this paper presents a stochastic attribute grammar model for leveraging complementary and discriminative human attributes for enhancing cross-view tracking. The key idea of our method is to introduce a hierarchical representation, parse graph, to describe a subject and its movement trajectory in both space and time domains. These results in a hierarchical compositional representation, comprising trajectory entities of varying level, including human boxes, 3D human boxes, tracklets, and trajectories. We use a set of grammar rules to decompose a graph node (e.g., tracklet) into a set of children nodes (e.g., 3D human boxes), and augment each node with a set of attributes, including geometry (e.g., moving speed and direction), accessories (e.g., bags), and/or activities (e.g., walking and running). These attributes serve as valuable cues, in addition to appearance features (e.g., colors), in determining the associations of human detection boxes across cameras. In particular, the attributes of a parent node are inherited by its children nodes, resulting in consistency constraints over the feasible parse graph. Thus, we cast cross-view human tracking as finding the most discriminative parse graph for each subject in videos. We develop a learning method to train this attribute grammar model from weakly supervised training data. To infer the optimal parse graph and its attributes, we develop an alternative parsing method that employs both top-down and bottom-up computations to search the optimal solution. We also explicitly reason the occlusion status of each entity in order to deal with significant changes of camera viewpoints. We evaluate the proposed method over public video benchmarks, and demonstrate with extensive experiments that our method clearly outperforms the state-of-the-art tracking methods. [ABSTRACT FROM AUTHOR]

It is very important to retrieve a specific person in locating and tracking the missing people as well as the suspects quickly. However, the well-studied face-based and appearance-based individual retrieval methods are ineffective in the surveillance scenarios because of the far photograph distances, the low camera resolutions, the long time intervals, and the complex lighting conditions. To avoid the disadvantages of face-based and appearance-based methods, we propose to retrieve individuals from the surveillance videos with the gait biometric, which has been proved to be beneficial to remote person recognition and robust to lighting variations. What’s more, the gait biometric can be collected without conscious cooperation, making the data collection much easier. But it varies greatly with the view angles, the clothing style, and the carrying conditions. Therefore, the videos of the target person from a similar view angle with the same clothing style and carrying conditions should rank higher than the others. To achieve this purpose and improve the efficiency, this paper proposes a kernel-based semantic hashing (KSH) model, which is learnt by optimizing a semantic triplet ranking loss. Specifically, in the training phase, a semantic similarity score, which depends on the view angles, the clothing style, and the carrying conditions, is calculated for each training pair. Then, a weighted triplet loss considering these semantic scores is designed, which encourages videos with a higher score to stay closer to the gallery in the binary Hamming space. To evaluate the performance of the proposed method, we compare it with several methods on the CASIA Gait Database B and the OU-ISIR Gait Database. The experimental results demonstrate that the KSH is effective and efficient. [ABSTRACT FROM AUTHOR]

Video-based person re-identification (re-id) has attracted a lot of research interest. When facing dramatic growth in new pedestrian videos, existing video-based person re-id methods usually need large quantities of labeled pedestrian videos to train a discriminative model. In practice, labeling large quantities of pedestrian videos is a costly and time-consuming task, which will limit the application of these methods in the real environment. Therefore, it is valuable and necessary to investigate how to learn a discriminative re-id model by using limited labeled training pedestrian videos. In this paper, we propose a semi-supervised cross-view projection-based dictionary learning (SCPDL) approach for video-based person re-id. Specifically, SCPDL jointly learns a pair of feature projection matrices and a pair of dictionaries by integrating the information contained in labeled and unlabeled pedestrian videos. With the learned feature projection matrices, the influence of variations within each video to the re-id can be reduced. With the learned dictionary pair, pedestrian videos from two different cameras can be converted into coding coefficients in a common representation space, such that the differences between different cameras can be bridged. In the learning process, the labeled pedestrian videos are used to ensure that the learned dictionaries have favorable discriminability; the large quantities of unlabeled pedestrian videos are used to ensure that SCPDL can better capture the variations between pedestrian videos, such that the learned dictionaries can own stronger representative capability. Experiments on two public pedestrian sequence data sets (iLIDS-VID and PRID 2011) demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

For semantic analysis of activities and events in videos, it is important to capture the spatio-temporal relation among objects in the 3D space. In this paper, we present a probabilistic method that extracts 3D trajectories of objects from 2D videos, captured from a monocular moving camera. Compared with existing methods that rely on restrictive assumptions, we propose a method that can extract 3D trajectories with much less restriction by adopting new example-based techniques, which compensate the lack of information. Here, we estimate the focal length of the camera based on similar candidates, and use it to compute depths of detected objects. Contrary to other 3D trajectory extraction methods, our method is able to process videos taken from a stable camera as well as a non-calibrated moving camera without restrictions. For this, we modify Reversible Jump Markov Chain Monte Carlo particle filtering to be more suitable for camera odometry without relying on geometrical feature points. Moreover, our method decreases time consumption by reducing the number of object detections with keypoint matching. Finally, we evaluate our method on known data sets by showing the robustness of our system and demonstrating its efficiency in dealing with different kind of videos. [ABSTRACT FROM AUTHOR]

Visible light cameras commonly used for surveillance applications usually have a limited field of view (FoV). To acquire a broader FoV, distributed aperture systems (DASs) combine views from multiple cameras. Although some commercial or proprietary systems already exist, the open literature in this field reports solely the performance and/or hardware architecture of the respective systems, and omits the required details for a reimplementation. In this paper we present a low-cost, personal-computer-based 360° DAS, with a full description of the hardware architecture and the software implementation details. In particular, we describe in detail two problems, the offline estimation of the orientations of the cameras of the proposed DAS and the online synthesis of a virtual view in real time. For the first problem, we propose an area-based bundle adjuster by combining the forward additive Lucas–Kanade algorithm with a bundle adjustment strategy. The color virtual view of up to $2048\times 1024$ pixels is synthesized online at 39 frames/s. A large majority of the workload of the online phase is implemented on the graphic processing unit. The techniques and methods proposed in this paper are generic and independent on the arrangement of cameras. [ABSTRACT FROM PUBLISHER]

Compressed sensing (CS) is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Exact reconstruction may then be possible with much fewer than the Nyquist-required number of data. In this paper, we consider a multiview video system in which multiple cameras at different locations perform independent CS to simultaneously capture different views of a scene. At the decoder, we propose a disparity- and motion-compensated total variation minimization algorithm to jointly reconstruct the multiview video sequence. The experimental results show that the proposed joint reconstruction algorithm successfully exploits simultaneously intra-frame, inter-frame, and inter-view sparsity and significantly outperforms existing independent-view reconstruction, residue-view reconstruction, and motion-adaptive reconstruction algorithms. [ABSTRACT FROM AUTHOR]

Near-range videos contain objects that are close to the camera. These videos often contain discontinuous depth variation (DDV), which is the main challenge to the existing video stabilization methods. Traditionally, 2D methods are robust to various camera motions (e.g., quick rotation and zooming) under scenes with continuous depth variation (CDV). However, in the presence of DDV, they often generate wobbled results due to the limited ability of their 2D motion models. Alternatively, 3D methods are more robust in handling near-range videos. We show that, by compensating rotational motions and ignoring translational motions, near-range videos can be successfully stabilized by 3D methods without sacrificing the stability too much. However, it is time-consuming to reconstruct the 3D structures for the entire video and sometimes even impossible due to rapid camera motions. In this paper, we combine the advantages of 2D and 3D methods, yielding a hybrid approach that is robust to various camera motions and can handle the near-range scenarios well. To this end, we automatically partition the input video into CDV and DDV segments. Then, the 2D and 3D approaches are adopted for CDV and DDV clips, respectively. Finally, these segments are stitched seamlessly via a constrained optimization. We validate our method on a large variety of consumer videos. [ABSTRACT FROM PUBLISHER]

Traditional image editing techniques cannot be directly used to process stereoscopic media, as extra constraints are required to ensure consistent changes between the left and right images. In this paper, we propose a hybrid warping model for stereoscopic image stitching by combining projective and content-preserving warping. First, a uniform homography algorithm is proposed to prewarp the left and right images, and thus ensure consistent changes. Second, a content-preserving warping is introduced to locally refine alignment and reduce vertical disparities. Finally, a seam-cutting-based algorithm is used to find a blending seam, and the multiband blending algorithm is used to produce the final stitched image. Experimental results show that the proposed method can effectively stitch stereoscopic images, which not only avoids local distortions, but also reduces vertical disparities reasonably. [ABSTRACT FROM PUBLISHER]

Amateur videos always contain focusing issues. A focusing mistake may produce out-of-focus blur, which seriously degrades the expressive force of the video. In this paper, we propose a patch-based method to remove the out-of-focus blur of a video and build an all-in-focus video. We assume that the out-of-focus blurry region in one frame will be clear in a portion of other frames; thus, the clear corresponding regions can be used to reconstruct the blurry one. We divide each video frame into a grid of patches and track each patch in the surrounding frames. We independently reconstruct each video frame by building a Markov random field model to identify the optimal target patches that are sharp, similar to the original patches, and are coherent with their neighboring patches within the overlapped regions. To recover an all-in-focus video, an iterative framework is utilized, in which the reconstructed video of each iteration is substituted in the next iteration. Finally, we employ the idea of a bilateral filter to temporally smooth the reconstructed video. The experimental results and the comparison with the previous works demonstrate the effectiveness of our method. [ABSTRACT FROM PUBLISHER]

The enormous growth of video content in recent times has raised the need to abbreviate the content for human consumption. Thus, there is a need for summaries of a quality that meets the requirements of human users. This also means that the summarization must incorporate the peculiar features of human perception. We present a new framework for video summarization in this paper. Unlike many available summarization algorithms that utilize only statistical redundancy, we introduce for the first time the features of the human visual system within the summarization framework itself to allow for the emphasis of perceptually significant events while simultaneously eliminating perceptual redundancy from the summaries. The subjective and objective evaluation scores have evaluated the framework. [ABSTRACT FROM PUBLISHER]

Development of a robust and scalable multicamera surveillance system is the need of the hour to ensure public safety and security. Being able to reidentify and track one or more targets over multiple nonoverlapping camera field of views in a crowded environment remains an important and challenging problem because of occlusions, large change in the viewpoints, and illumination across cameras. However, the rise of wearable imaging devices has led to new avenues in solving the reidentification (re-id) problem. Unlike static cameras, where the views are often restricted or low resolution and occlusions are common scenarios, egocentric/first person views (FPVs) mostly get zoomed in, unoccluded face images. In this paper, we present a person re-id framework designed for a network of multiple wearable devices. The proposed framework builds on commonly used facial feature extraction and similarity computation methods between camera pairs and utilizes a data association method to yield globally optimal and consistent re-id results with much improved accuracy. Moreover, to ensure its utility in practical applications where a large amount of observations are available every instant, an online scheme is proposed as a direct extension of the batch method. This can dynamically associate new observations to already observed and labeled targets in an iterative fashion. We tested both the offline and online methods on realistic FPV video databases, collected using multiple wearable cameras in a complex office environment and observed large improvements in performance when compared with the state of the arts. [ABSTRACT FROM PUBLISHER]

This paper proposes an ensemble of invariant features (EIFs), which can properly handle the variations of color difference and human poses/viewpoints for matching pedestrian images observed in different cameras with nonoverlapping field of views. Our proposed method is a direct reidentification (re-id) method, which requires no prior domain learning based on prelabeled corresponding training data. The novel features consist of the holistic and region-based features. The holistic features are extracted by using a publicly available pretrained deep convolutional neural network used in generic object classification. In contrast, the region-based features are extracted based on our proposed two-way Gaussian mixture model fitting, which overcomes the self-occlusion and pose variations. To make a better generalization during recognizing identities without additional learning, the ensemble scheme aggregates all the feature distances using the similarity normalization. The proposed framework achieves robustness against partial occlusion, pose, and viewpoint changes. Moreover, the evaluation results show that our method outperforms the state-of-the-art direct re-id methods on the challenging benchmark viewpoint invariant pedestrian recognition and 3D people surveillance data sets. [ABSTRACT FROM PUBLISHER]

Spatiotemporal data association and fusion is a well-known NP-hard problem even in a small number of cameras and frames. Although it is difficult to be tractable, solving them is pivotal for tracking in a multicamera network. Most approaches model association maladaptively toward properties and contents of video, and hence they produce suboptimal associations and association errors propagate over time to adversely affect fusion. In this paper, we present an online multicamera multitarget tracking framework that performs adaptive tracklet correspondence by analyzing and understanding contents and properties of video. Unlike other methods that work only on synchronous videos, our approach uses dynamic time warping to establish correspondence even if videos have linear or nonlinear time asynchronous relationship. Association is a two-stage process based on geometric and appearance descriptor space ranked by their inter- and intra-camera consistency and discriminancy. Fusion is reinforced by weighting the associated tracklets with a confidence score calculated using reliability of individual camera tracklets. Our robust ranking and election learning algorithm dynamically selects appropriate features for any given video. Our method establishes that, given the right ensemble of features, even computationally efficient optimization yields better accuracy in tracking over time and provides faster convergence that is suitable for real-time application. For evaluation on RGB, we benchmark on multiple sequences in PETS 2009 and we achieve performance that is on par with the state of the art. For evaluating on RGB-D, we built a new data set. [ABSTRACT FROM PUBLISHER]

With the advance of coding and network technology, the multiview video (MVV) system has received growing attention in the 3-D TV market since it can provide consumers with more realistic scenes from different viewpoints. In the MVV system, each video sequence consists of one color-map video and one depth video to be encoded and the depth-image-based rendering technique is often used to create/synthesize virtual views for different viewpoints. To attain the same quality of virtual views, encoding depth videos in a fast and bitrate-saving manner is crucial. Based on the depth no-synthesis-error model developed by Zhao et al., this paper proposes a new intra-/inter-prediction scheme first and then a fast quadtree structure determination scheme for encoding depth videos in 3-D High Efficiency Video Coding (3D-HEVC). The first proposed scheme has bitrate-saving merit because for each coding unit, each depth value is modified to approach the predicted intra-/inter-depth value as closely as possible, but without causing any synthesis errors in the resultant virtual views. The second proposed scheme has the merit of low computational cost because it can terminate the quadtree structure determination for coding tree units as early as possible. Based on four typical test video sequences, the empirical results demonstrate that on average, our proposed encoding method for depth videos has 16.3% bitrate and 13.8% encoding-time improvement ratios when compared with the traditional method in 3D-HEVC test model, while preserving the quality of the rendered virtual views. In addition, our proposed method outperforms that of Lee et al. in terms of the required bitrate and encoding time. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive study on sensor-assisted video encoding (SaVE) schemes for video capturing on mobile devices in real-world environments. Our purpose is to reduce the computational complexity of video encoding by leveraging sensors that are increasingly available on mobile devices, e.g., accelerometers and digital compasses. Motion estimation is a key component of video encoding. In this paper, SaVE calculates the rotational movement of a camera (on mobile devices) and then infers the global motion in the camera imager. SaVE subsequently employs the estimated global motion as predictors to simplify motion estimation algorithms for state-of-the-art H.264/AVC video coding. We have constructed a prototype of SaVE and evaluated its performance with a pair of accelerometers, a digital compass, and their combination. Our experimental results show that SaVE can significantly reduce the computations of motion estimation while achieving equal or better video quality. Our results also show that SaVE has a strong noise-resistant capability. Therefore, it can be practically employed in real-world environments. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new method for detecting primary objects in unconstrained videos in a completely automatic setting. Here, we define the primary object in a video as the object that presents saliently in most of the frames. Unlike previous works considering only local saliency detection or common pattern discovery, the proposed method integrates the local visual/motion saliency extracted from each frame, global appearance consistency throughout the video, and spatiotemporal smoothness constraint on object trajectories. We first identify a temporal coherent salient region throughout the whole video, and then explicitly learn a global appearance model to distinguish the primary object against the background. In order to obtain high-quality saliency estimations from both appearance and motion cues, we propose a novel self-adaptive saliency map fusion method by learning the reliability of saliency maps from labeled data. As a whole, our method can robustly localize and track primary objects in diverse video content, and handle the challenges such as fast object and camera motion, large scale and appearance variation, background clutter, and pose deformation. Moreover, compared with some existing approaches that assume the object is present in all the frames, our approach can naturally handle the case where the object is present only in part of the frames, e.g., the object enters the scene in the middle of the video or leaves the scene before the video ends. We also propose a new video data set containing 51 videos for primary object detection with per-frame ground-truth labeling. Quantitative experiments on several challenging video data sets demonstrate the superiority of our method compared with the recent state of the arts. [ABSTRACT FROM PUBLISHER]

Human activity recognition has a variety of important real-world applications, such as video analysis, surveillance, and human-robot interaction. As a promising video representation method, local spatio-temporal (LST) features have received increasing attention from computer vision, machine learning, and robotics communities. However, approaches based on traditional LST features only use color information, which face several challenges, such as illumination changes and dynamic backgrounds. The recent availability of commercial color-depth cameras makes it much cheaper, faster, and easier to acquire depth information, which provides a potential to implement more discriminative and robust LST features. In this paper, we introduce the new 4-D color-depth (CoDe4D) LST feature that incorporates both intensity and depth information acquired from RGB-D cameras. Our feature detector constructs a saliency map through applying independent filters in \boldsymbol xyzt dimension to represent texture, shape and pose variations, and selects its local maxima as interest points. Our multichannel orientation histogram descriptor applies a 4-D support region, which is adaptive to linear perspective view changes, on each interest point. Then, image gradients of color-depth patches within the support region are computed and quantized using a spherical coordinate-based method to form a final feature vector. We build a complete activity recognition system by combining our features with bag-of-features representations and support vector machines. To evaluate the performance of our CoDe4D LST features and the complete system, we conduct experiments using four benchmark color-depth human activity data sets, including UTK Action3-D, Berkeley MHAD, ACT \boldsymbol 4^2^^^ , and MSR daily activity 3-D data sets. Experimental results demonstrate the promising representative power of our CoDe4D features, which obtain the state-of-the-art performance on activity recognition from RGB-D visual data. [ABSTRACT FROM AUTHOR]

The emergence of new wearable technologies, such as action cameras and smart glasses, has increased the interest of computer vision scientists in the first person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with first person vision (FPV) recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real time, is expected. The current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user–machine interaction, and so on. This paper summarizes the evolution of the state of the art in FPV video analysis between 1997 and 2014, highlighting, among others, the most commonly used features, methods, challenges, and opportunities within the field. [ABSTRACT FROM AUTHOR]

Nonextractive fish abundance estimation with the aid of visual analysis has drawn increasing attention. Unstable illumination, ubiquitous noise, and low-frame-rate (LFR) video capturing in the underwater environment, however, make conventional tracking methods unreliable. In this paper, we present a multiple fish-tracking system for low-contrast and LFR stereo videos with the use of a trawl-based underwater camera system. An automatic fish segmentation algorithm overcomes the low-contrast issues by adopting a histogram backprojection approach on double local-thresholded images to ensure an accurate segmentation on the fish shape boundaries. Built upon a reliable feature-based object matching method, a multiple-target tracking algorithm via a modified Viterbi data association is proposed to overcome the poor motion continuity and frequent entrance/exit of fish targets under LFR scenarios. In addition, a computationally efficient block-matching approach performs successful stereo matching that enables an automatic fish-body tail compensation to greatly reduce segmentation error and allows for an accurate fish length measurement. Experimental results show that an effective and reliable tracking performance for multiple live fish with underwater stereo cameras is achieved. [ABSTRACT FROM AUTHOR]

The emergence of pico projectors as a part of future mobile devices presents unique opportunities for collaborative settings, especially in entertainment applications, such as video playback. By aggregating pico projectors from several users, it is possible to enhance resolution, brightness, or frame rate. In this paper we present a camera-based methodology for the alignment and synchronization of multiple projectors. The approach does not require any complicated ad hoc network setup among the mobile devices. A prototype system has been set up and used to test the proposed techniques. [ABSTRACT FROM AUTHOR]

Human group activities detection in multi-camera CCTV surveillance videos is a pressing demand on smart surveillance. Previous works on this topic are mainly based on camera topology inference that is hard to apply to real-world unconstrained surveillance videos. In this paper, we propose a new approach for multi-camera group activities detection. Our approach simultaneously exploits intra-camera and inter-camera contexts without topology inference. Specifically, a discriminative graphical model with hidden variables is developed. The intra-camera and inter-camera contexts are characterized by the structure of hidden variables. By automatically optimizing the structure, the contexts are effectively explored. Furthermore, we propose a new spatiotemporal feature, named vigilant area (VA), to characterize the quantity and appearance of the motion in an area. This feature is effective for group activity representation and is easy to extract from a dynamic and crowded scene. We evaluate the proposed VA feature and discriminative graphical model extensively on two real-world multi-camera surveillance video data sets, including a public corpus consisting of 2.5 h of videos and a 468-h video collection, which, to the best of our knowledge, is the largest video collection ever used in human activity detection. The experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM PUBLISHER]

We address the problem of camera calibration for traffic scene surveillance, which supplies a connection between 2-D image features and 3-D measurement. It is helpful to deal with appearance distortion related to view angles, establish multiview correspondences, and make use of 3-D object models as prior information to enhance surveillance performance. A convenient and practical camera calibration method is proposed in this paper. With the camera height H measured as the only user input, we can recover both intrinsic and extrinsic parameters of the camera based on redundant information supplied by moving objects in monocular videos. All cases of traffic scene layouts are considered and corresponding solutions are given to make our method applicable to almost all kinds of traffic scenes in reality. Numerous experiments are conducted in different scenes, and experimental results demonstrate the accuracy and practicability of our approach. It is shown that our approach can be effectively adopted in all kinds of traffic scene surveillance applications. [ABSTRACT FROM PUBLISHER]

In this paper, we put forward a novel region matching-based motion estimation scheme to detect objects with accurate boundaries from videos captured by moving camera. Here, a fuzzy edge incorporated Markov random field (MRF) model is considered for spatial segmentation. The algorithm is able to identify even the blurred boundaries of objects in a scene. Expectation Maximization algorithm is used to estimate the MRF model parameters. To reduce the complexity of searching, a new scheme is proposed to get a rough idea of maximum possible shift of objects from one frame to another by finding the amount of shift in positions of the centroid. We propose a \chi^2-test-based local histogram matching scheme for detecting moving objects from complex scenes from low illumination environment and objects that change size from one frame to another. The proposed scheme is successfully applied for detecting moving objects from video sequences captured in both real-life and controlled environments. It is also noticed that the proposed scheme provides better results with less object background misclassification as compared to existing techniques. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel geometry video (GV) framework to model and compress 3-D facial expressions. GV bridges the gap of 3-D motion data and 2-D video, and provides a natural way to apply the well-studied video processing techniques to motion data processing. Our framework includes a set of algorithms to construct GVs, such as hole filling, geodesic-based face segmentation, expression-invariant parameterization (EIP), and GV compression. Our EIP algorithm can guarantee the exact correspondence of the salient features (eyes, mouth, and nose) in different frames, which leads to GVs with better spatial and temporal coherence than that of the conventional parameterization methods. By taking advantage of this feature, we also propose a new H.264/AVC-based progressive directional prediction scheme, which can provide further 10%–16% bitrate reductions compared to the original H.264/AVC applied for GV compression while maintaining good video quality. Our experimental results on real-world datasets demonstrate that GV is very effective for modeling the high-resolution 3-D expression data, thus providing an attractive way in expression information processing for gaming and movie industry. [ABSTRACT FROM PUBLISHER]

Advances in cameras and web technology have made it easy to capture and share large amounts of video data over to a large number of people. A large number of cameras oversee public and semi-public spaces today. These raise concerns on the unintentional and unwarranted invasion of the privacy of individuals caught in the videos. To address these concerns, automated methods to de-identify individuals in these videos are necessary. De-identification does not aim at destroying all information involving the individuals. Its ideal goals are to obscure the identity of the actor without obscuring the action. This paper outlines the scenarios in which de-identification is required and the issues brought out by those. We also present an approach to de-identify individuals from videos. Our approach involves tracking and segmenting individuals in a conservative voxel space involving x, y, and time. A de-identification transformation is applied per frame using these voxels to obscure the identity. Face, silhouette, gait, and other characteristics need to be obscured, ideally. We show results of our scheme on a number of videos and for several variations of the transformations. We present the results of applying algorithmic identification on the transformed videos. We also present the results of a user-study to evaluate how well humans can identify individuals from the transformed videos. [ABSTRACT FROM PUBLISHER]

In surveillance applications, keyframes are usually used to summarize important video content. However, traditional keyframe extracting methods select video frames as keyframes directly from the input video, and the information in these selected video frames is scattered and the users cannot perceive how the events happened in the original video easily. In this paper, we propose a new type of keyframe (called an “augmented 3-D keyframe”) that is a more meaningful and compact keyframe, augmented with representative objects, important contents (human faces, license plates, and so on), motion information (including trajectories and simple movement situations), and some marks of the moving objects extracted from a surveillance video clip captured by a static camera. The innovation is that we include simple 3-D geometric information to generate this augmented 3-D keyframe without reconstructing a complete 3-D scene. Testing results of the proposed augmented 3-D keyframe are presented and compared with results obtained by the traditional keyframe approach. [ABSTRACT FROM PUBLISHER]

In this paper, we present an alternative framework for video processing without explicit motion estimation or segmentation. Motivated by the geometric constraint of motion trajectory, we propose an adaptive filtering-based model for video signals in which filter coefficients are locally estimated by the least-square method. Such localized estimation can be viewed as an implicit approach of exploiting motion-related temporal dependency. We also introduce the the concept of a virtual camera to further improve the modeling capability by exploiting the fundamental tradeoff between space and time. Using mixture models, we show how to probabilistically fuse the inference results obtained from virtual cameras in order to achieve spatio-temporal adaptation. Implicit and mixture motion model supplements the existing paradigm and provides a unified solution to a wide range of low-level vision problems including video dejittering, impulse removal, error concealment, video coding, and temporal interpolation. [ABSTRACT FROM AUTHOR]