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Video Anomaly Detection Based on Hierarchical Clustering

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Human Centered Computing (HCC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11354))

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Abstract

Recently the video surveillance market has developed rapidly, but judging whether there is abnormal behavior in the video relying on manpower is too expensive. Therefore, a method is needed to identify the abnormal behavior automatically. Scholars at home and abroad have done in-depth research on video abnormal events detection in different scenarios. However, the current detection technology still needs improvement in the speed of the algorithm. From this point of view, this paper proposes a video abnormal event detection method based on hierarchical clustering.

In order to construct the sparse coefficient matrix more accurately and quickly, the hierarchical clustering is introduced into sparse coding in this paper. And the structure information of the sparse coefficient matrix is used as the clustering criteria, which improves the standard group sparse coding method. In addition, the BK-SVD algorithm is used to train the dictionary so that we can further improve the speed of the algorithm through dictionary division. In the experimental part, we prove that the proposed algorithm has great performance in frame level and pixel level in MATLAB environment.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 61731003, the projects of International Cooperation and Exchanges NFSC under Grant 61520106002.

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Authors and Affiliations

  1. School of Electrical Automation and Information Engineering, Tianjin University, Tianjin, 300072, China

    Chunyue Zhao, Beichen Li, Qing Wang & Zhipeng Wang

Authors
  1. Chunyue Zhao
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  2. Beichen Li
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  3. Qing Wang
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  4. Zhipeng Wang
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Corresponding author

Correspondence to Zhipeng Wang .

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Editors and Affiliations

  1. South China Normal University, Guangzhou, China

    Yong Tang

  2. Wuhan University of Technology, Wuhan City, China

    Qiaohong Zu

  3. CINVESTAV, Mexico City, Mexico

    José G. Rodríguez García

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Zhao, C., Li, B., Wang, Q., Wang, Z. (2019). Video Anomaly Detection Based on Hierarchical Clustering. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_55

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  • DOI: https://doi.org/10.1007/978-3-030-15127-0_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15126-3

  • Online ISBN: 978-3-030-15127-0

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