Abstract
In the field of activity recognition, violence detection is one of the most challenging tasks due to the variety of action patterns and the lack of training data. In the last decade, the performance is getting improved by applying local spatio-temporal features. However, geometric relationships and transition processes of these features have not been fully utilized. In this paper, we propose a novel framework based on spatio-temporal hypergraph transition. First, we utilize hypergraphs to represent the geometric relationships among spatia-temporal features in a single frame. Then, we apply a new descriptor called Histogram of Velocity Change (HVC), which characterizes motion changing intensity, to model hypergraph transitions among consecutive frames. Finally, we adopt Hidden Markov Models (HMMs) with the hypergraphs and the descriptors to detect and localize violence in video frames. Experiment results on BEHAVE dataset and UT-Interaction dataset show that the proposed framework outperforms the existing methods.
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Acknowledgments
This work was supported by National Science Foundation of China (No. U1611461), National Natural Science Foundation of China (61602014), Shenzhen Peacock Plan (20130408-183003656), and Science and Technology Planning Project of Guangdong Province, China (No. 2014B090910001).
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Huang, J., Li, G., Li, N., Wang, R., Wang, W. (2017). A Violence Detection Approach Based on Spatio-temporal Hypergraph Transition. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_19
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DOI: https://doi.org/10.1007/978-3-319-64698-5_19
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