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Image quality assessment (IQA) is an important task of image processing and has diverse applications, such as image super-resolution reconstruction, image transmission and monitoring systems. This paper proposes a perceptual hashing algorithm with complementary color wavelet transform (CCWT) and compressed sensing (CS) for reduced-reference (RR) IQA. The CCWT is exploited to decompose input color image into different sub-bands. Since the calculation of CCWT uses all color channels without discarding any information, the distortions introduced by digital operations on color channels are preserved in the CCWT sub-bands. The block-based CS is used to extract features from the CCWT sub-bands. As the Euclidean distance between the block-based CS features is slightly influenced by content-preserving operations, perceptual features constructed by Euclidean distances are robust, discriminative and compact. Hash sequence is finally determined by quantifying the perceptual features. Effectiveness of the proposed hashing is verified by various experiments on four open image databases. Experimental results demonstrate that the proposed hashing is superior to some state-of-the-art algorithms in terms of classification and RR IQA application. [ABSTRACT FROM AUTHOR]

The performance of correlation filter (CF) based visual trackers has been greatly improved with pretrained deep convolutional neural networks. However, these networks limit the application scope of CF based trackers because of high feature dimension, high time consumption of feature extraction and huge memory storage. To alleviate this problem, we introduce a teacher-student knowledge distillation framework to obtain a lightweight network to speed up CF based trackers. Specifically, we take a pretrained deep convolutional neural network from the image classification task as a teacher network, and distill this teacher network into a lightweight student network. During offline distillation training process, we propose an attention transfer loss to ensure the lightweight student network maintains feature representation of the large-capacity teacher network. Meanwhile, we propose a correlation tracking loss to transfer the student network from image classification task to correlation tracking task, which improves the discriminant ability of the student network. Experiments on OTB, VOT2017 and Temple Color show that, using the learned lightweight network model as the feature extractor, the state-of-the-art CF based tracker achieves real-time speed on a single CPU, while maintaining almost the same tracking performance. [ABSTRACT FROM AUTHOR]

Road extraction from remote sensing images is an attractive but difficult task. Gray-value distribution and structure feature information are both crucial for road extraction task. However, existing methods mainly focus on structure feature information which contains morphological shape features and machine learning features, suffering from lots of false positives which are generated at positions having similar structure features but different gray-value distribution with roads. To effectively fuse the two complementary gray-value distribution and structure feature information, we propose a coarse-to-fine road extraction algorithm from remote sensing images. First, at the coarse level, we introduce a local Dirichlet mixture models (LDMM) which utilizing gray-value distribution information to pre-segment images into potential roads and backgrounds. Thus, most backgrounds having different gray-value distribution with roads can be removed firstly. Compared with original Dirichlet mixture models, the LDMM is much faster and more accurate. Next, at the fine level, we introduce a multiscal-high-order deep learning strategy based on ResNet model which can learn robust structure context features for final road extraction step. Based on the results of LDMM, the multiscal-high-order strategy can further remove false positives which have different structure features with roads. Compared with a single scanning size ResNet, our multiscale-high-order strategy can learn higher-order context information, leading to better performances. We test our algorithm on Shaoshan dataset. Experiments illustrate our better performance compared with other six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With a large number of video surveillance systems installed for the requirement from industrial security, the task of object tracking, which aims to locate objects of interest in videos, is very important. Although numerous tracking algorithms for RGB videos have been developed in the decade, the tracking performance and robustness of these systems may be degraded dramatically when the information from RGB video is unreliable (e.g., poor illumination conditions or very low resolution). To address this issue, this paper presents a new tracking system, which aims to combine the information from RGB and infrared modalities for object tracking. The proposed tracking systems is based on our proposed machine learning model. Particularly, the learning model can alleviate the modality discrepancy issue under the proposed modality consistency constraint from both representation patterns and discriminability, and generate discriminative feature templates for collaborative representations and discrimination in heterogeneous modalities. Experiments on a variety of challenging RGB-infrared videos demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Tracking-by-detection is one of the typical paradigms for multi-person tracking, due to the availability of automatic pedestrian detectors. However, existing multi-person trackers are greatly challenged by misalignment in the pedestrian detectors (i.e., excessive background and part missing) and occlusion. To effectively handle these problems, we propose a deep alignment network-based multi-person tracking method with occlusion and motion reasoning. Specifically, the inaccurate detections are first corrected via a deep alignment network, in which an alignment estimation module is used to automatically learn the spatial transformation of these detections. As a result, the deep features from our alignment network will have better representation power and, thus, lead to more consistent tracks. Then, a coarse-to-fine schema is designed for construing a discriminative association cost matrix with spatial, motion, and appearance information. Meanwhile, a principled approach is developed to allow our method to handle occlusion with motion reasoning and the reidentification ability of the pedestrian alignment network. Finally, the association problem is solved via a simple yet real-time Hungarian algorithm. Comprehensive experiments on MOT16, ISSIA soccer, PETS09, and TUD datasets validate the effectiveness and robustness of our proposed tracker. [ABSTRACT FROM AUTHOR]

This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different from existing uniform threshold based motion detection approaches which are only effective for simple background, the kernel similarity modeling is proposed to produce an adaptive threshold for complex background. The adaptive threshold is computed from the mean and variance of an extended Gaussian mixture model. The proposed KSM-TPF approach incorporates machine learning method with feature extraction method in a homogenous way. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection. [ABSTRACT FROM PUBLISHER]

Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human–object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition. [ABSTRACT FROM AUTHOR]