Abstract
[Objective] Video surveillance technology is more and more used in all kinds of scenes, and a large number of surveillance videos are produced every day. In the video, most of the data are normal events, however people focus on only a few abnormal events, how to find individual abnormal events in the massive video is the focus of current research. [Methods] Firstly, the dense optical flow of video is obtained, and the information of optical flow is transformed into the histogram feature of optical flow. Secondly, the space-time cube of video is constructed by using the space-time correlation of video. Finally, sparse representation method is used to model the whole process. [Result] The part in the video with too much reconstruction error is considered as abnormal event. Experiments on the UCSD dataset show that proposed method can effectively detect the abnormal events in the video. [Conclusion] In this paper, a video anomaly event feature and an anomaly detection model are proposed, which provide support for further research.
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Acknowledgments
This work is supported by the Natural Science Fund of Gansu (No. 17JR5RA161 and No. 18JR3RA193), Scientific Research project of Gansu University of Political Science and Law (No. 2017XQNLW14), and Project of Innovation Ability Improvement of Colleges in Gansu Province (No. 2019A-091).
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Li, Z., Yang, W., Wu, G., Liu, L. (2021). Unsupervised Video Anomaly Detection Based on Sparse Reconstruction. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_143
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DOI: https://doi.org/10.1007/978-981-33-4572-0_143
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