Abstract
Motion is the most informative cue for human action recognition. Regions with high motion saliency indicate where actions occur and contain visual information that is most relevant to actions. In this paper, we propose a novel approach for human action recognition based on oriented motion salient regions (OMSRs). Firstly, we apply a bank of 3D Gabor filters and an opponent inhibition operator to detect OMSRs of videos, each of which corresponds to a specific motion direction. Then, a new low-level feature, named as oriented motion salient descriptor (OMSD), is proposed to describe the obtained OMSRs through the statistics of the texture in the regions. Next, we utilize the obtained OMSDs to explore the oriented characteristics of action classes and generate a set of class-specific oriented attributes (CSOAs) for each class. These CSOAs provide a compact and discriminative middle-level representation for human actions. Finally, an SVM classifier is utilized for human action classification and a new compatibility function is devised for measuring how well a given action matches to the CSOAs of a certain class. We test the proposed approach on four public datasets and the experimental results validate the effectiveness of our approach.
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Acknowledgement
This work is partly supported by the 973 basic research program of China (Grant No. 2014CB349303), the National 863 High-Tech R&D Program of China (Grant No. 2012AA012504), the Natural Science Foundation of Beijing (Grant No. 4121003), the Project Supported by Guangdong Natural Science Foundation (Grant No. S2012020011081) and NSFC (Grant No. 61100099, 61303086).
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Wu, B., Yang, S., Yuan, C., Hu, W., Wang, F. (2015). Human Action Recognition Based on Oriented Motion Salient Regions. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_9
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DOI: https://doi.org/10.1007/978-3-319-16628-5_9
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