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Labanotation is a symbolic recording system for human movements, and also a powerful tool for protecting and spreading folk dances and other performing arts. State-of-the-art automatic Labanotation uses end-to-end methods with sequence-based skeleton representation, which cannot capture the relationship between joints and bones in the skeleton for accurate descriptions of continuous lower limb movements such as dance steps. In this paper, we propose a novel double-stream fusion method of directed graph neural networks (DGNN), combined with connectionist temporal classification (CTC), namely DFGNN-CTC, for sequential fine-grained motion recognition, such as the Labanotation generation of unsegmented dance movement. First, we extract double-stream directed graph feature, employing an orientation-normalized directed acyclic graph (ON-DAG) and an orientation-normalized temporal directed acyclic graph (ON-TDAG), to jointly model spatiotemporal properties of movement recorded in motion capture data. Then, we design a CTC-based fusion-pooling module to fuse the spatial and temporal streams encoded by two DGNNs. It concatenates and fuses the two streams to generate discriminative descriptions of each time step, and concentrates them to make per-time-step predictions of Laban gesture type, from which the CTC searches the optimal Laban symbol sequence, corresponding to elemental motions composing the movement. In this way, the new method enables much finer discrimination for similar Laban gestures with subtle differences in spatial and temporal properties through joint contextual spatiotemporal modeling so that it achieves much superior performance in continuous Labanotation generation to existing methods, which only have single-stream analysis either spatially or temporally. The experiments on two Labanotation-labelled motion capture datasets demonstrate the effectiveness of the components in the proposed method and its superiority comparing with the state-of-the-art methods, especially for lower limb movements. [ABSTRACT FROM AUTHOR]

Human activity localization aims at recognizing contents and detecting locations of activities in video sequences. With an increasing number of untrimmed video data, traditional activity localization methods always suffer from two major limitations. First, detailed annotations are needed in most existing methods, i.e., bounding-box annotations in every frame, which are both expensive and time consuming. Second, the search space is too large for 3D activity localization, which requires generating a large number of proposals. In this paper, we propose a unified deep Q-network with weak reward and weak loss (DWRLQN) to address the two problems. Certain weak knowledge and weak constraints involving the temporal dynamics of human activity are incorporated into a deep reinforcement learning framework under sparse spatial supervision, where we assume that only a portion of frames are annotated in each video sequence. Experiments on UCF-Sports, UCF-101 and sub-JHMDB demonstrate that our proposed model achieves promising performance by only utilizing a very small number of proposals. More importantly, our DWRLQN trained with partial annotations and weak information even outperforms fully supervised methods. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the RS techniques, a large number of RS scene images are available. As manually labeling large-scale RS scene images is both labor and time consuming, when a new unlabeled data set is obtained, how to use the existing labeled data sets to classify the new unlabeled images is an important research direction. Different RS scene image data sets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions, and image scales, so the distribution discrepancy exists among different image data sets. As a result, simply applying convolutional neural networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain adaptation (DA) can be helpful to solve this problem. In this letter, we design a subspace alignment (SA) and CNN-based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Fine-tuning the modified CNN model with the added SA layer makes the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public data sets show that adding the SA layer into CNN improves the scene classification on the target domain. [ABSTRACT FROM AUTHOR]

As a major research field in computer vision, automatic generation of Labanotation has been attracting the attention of many researchers. These researchers use various methods to engage in dance education and protection, but the existing methods do not consider the spatial-temporal dependence modeling of dance movements. Therefore, they do not represent complex dance movements optimally. We propose an automatic generation network of Labanotation based on the long and short spatial-temporal relations. It includes local spatial-temporal feature extraction network, global spatial-temporal feature extraction network, and local and global feature fusion network. It can align the input and output sequences while modeling the skeletal spatial-temporal relation. The local spatial-temporal features among short interval skeleton sequence relationship are obtained through the multi-scale convolution of time and space. The global spatial-temporal features are learned through the transformer network to obtain the relationship among skeleton sequences with long interval. For the output of the two networks, we use the pyramid squeeze attention network to exchange information in long and short spatial-temporal information, achieving complementarity to improve the accuracy of action recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods on laban16 and laban48, which are common datasets for the research of Labanotation. [ABSTRACT FROM AUTHOR]

There are two key issues in human activity recognition: spatial dependencies and temporal dependencies. Most recent methods focus on only one of them, and thus do not have sufficient descriptive power to recognize complex activity. In this paper, we propose a hierarchical spatio-temporal model (HSTM) to solve the problem by modeling spatial and temporal constraints simultaneously. The new HSTM is a two-layer hidden conditional random field (HCRF), where the bottom-layer HCRF aims at describing spatial relations in each frame and learning more discriminative representations, and the top-layer HCRF utilizes these high-level features to characterize temporal relations in the whole video sequence. The new HSTM takes advantage of the bottom layer as the building blocks for the top layer and it aggregates evidence from local to global level. A novel learning algorithm is derived to train all model parameters efficiently and its effectiveness is validated theoretically. Experimental results show that the HSTM can successfully classify human activities with higher accuracies on single-person actions (UCF) than other existing methods. More importantly, the HSTM also achieves superior performance on more practical interactions, including human–human interactional activities (UT-Interaction, BIT-Interaction, and CASIA) and human–object interactional activities (Gupta video dataset). [ABSTRACT FROM PUBLISHER]

Recent works have shown that mid-level feature is superior to low-level feature, which can not only improve discriminative power, but also enhance descriptive capability. In this paper, the classical STIP, spatial star-graph and temporal star-graph are first extracted to represent human action from multi-perspectives. Then a principled feature learning algorithm is proposed to embed these multi-cues into a unified space and enhance all low-level features using diffusion map. Unlike treating spatio-temporal patch as mid-level primitive, we use a graph to model different types of primitives, then apply graph partitioning to co-cluster them into visual-word clusters called mid-level distinctive feature, which can bridge the semantic gap across low-level features. Experimental results show that our approach can successfully classify human activities with higher accuracies both on single-person actions (KTH and UCF) and complex interactional activities (UT-Interaction and HMDB51). [ABSTRACT FROM AUTHOR]

Abnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect abnormal events in crowded scenes. Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. Compared with other methods based on the optical flow, experiments on several benchmark datasets show that our algorithm is effective with satisfying results. [ABSTRACT FROM AUTHOR]

The appearance gap between sketches and photo- realistic images is a fundamental challenge in sketch-based image retrieval (SBIR) systems. The existence of noisy edges on photo- realistic images is a key factor in the enlargement of the appearance gap and significantly degrades retrieval performance . To bridge the gap, we propose a framework consisting of a new line segment -based descriptor named histogram of line relationship (HLR) and a new noise impact reduction algorithm known as object boundary selection . HLR treats sketches and extracted edges of photo- realistic images as a series of piece-wise line segments and captures the relationship between them. Based on the HLR, the object boundary selection algorithm aims to reduce the impact of noisy edges by selecting the shaping edges that best correspond to the object boundaries. Multiple hypotheses are generated for descriptors by hypothetical edge selection. The selection algorithm is formulated to find the best combination of hypotheses to maximize the retrieval score; a fast method is also proposed. To reduce the distraction of false matches in the scoring process, two constraints on spatial and coherent aspects are introduced . We tested the HLR descriptor and the proposed framework on public datasets and a new image dataset of three million images, which we recently collected for SBIR evaluation purposes. We compared the proposed HLR with state-of-the-art descriptors (SHoG, GF-HOG). The experimental results show that our HLR descriptor outperforms them. Combined with the object boundary selection algorithm, our framework significantly improves SBIR performance. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new image completion method based on high-accuracy semantic matching. Existing image completion approaches mainly focus on simple filling, ignoring creativity and accuracy in semantic matching. Our method can complete missing regions with more creative and semantically matching images as well as create a more seamless and consistent completed image. We use global and local features to search matching images for the target image. We then complete the missing region using Poisson blending and blending optimization. Results of experiments on challenging image databases show that the images are greatly improved. Thus, these results demonstrate the superiority of the proposed algorithm over existing image completion approaches. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the improvements of appearance representation for multihuman tracking. Many previous methods extracted low-level appearance features, such as color histogram and texture, even combined with spatial information for each frame. These methods ignore the temporal distribution of features. The features of each frame may not be stable due to illumination, human pose variation, and image noise. In order to improve it, we propose a novel appearance representation called the spatial–temporal appearance model based on the statistical distribution of Gaussian mixture model (GMM). It represents the appearance of a tracklet as a whole with dynamic spatial and temporal information. The spatial information is the dynamic subregions. The temporal information is the dynamic duration time of each subregion. Each subregion is modeled as the weighted Gaussian distribution of GMM. The online expectation-maximization (online EM) algorithm is used to estimate the parameters of GMM. Then, we propose a tracklet association method using Bayesian prediction and Jensen–Shannon divergence. The Bayesian prediction is used to predict the locations of targets. The Jensen–Shannon divergence is used to compute the distance of spatial–temporal appearance distribution between two tracklets. Finally, we test our approach on four challenging datasets (TRECVID, CAVIAR, ETH, and EPFL Terrace) and achieve good results. [ABSTRACT FROM PUBLISHER]

Multiperson activity recognition in videos is a challenging task, due to the complexity of interactions among multiple persons. In this paper, a new statistical model, named coupled observation decomposed hidden Markov model (CODHMM), is presented to model multiperson activities in videos. A human activity that involves multiple persons is analyzed in two levels: the individual level that describes each individual's motion details and the interaction level that expresses the shared information among multiple persons. The two levels are modeled by two hidden Markov chains that are interdependent and interact with each other. The observation in each chain at each time slice is decomposed into subobservations according to the number of features and the number of persons. For each activity to be recognized, a CODHMM is built and model parameters are learnt by a generalized expectation maximization (EM) algorithm. Given an input video that contains an unknown activity, maximum likelihood algorithms are developed to classify it into one of the learnt activity categories. Experimental results show that the CODHMM can successfully classify human activities involving multiple persons with high accuracy and low computations. [ABSTRACT FROM AUTHOR]