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Anomaly detection based on sparse coding with two kinds of dictionaries

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Abstract

In this paper, we propose a novel method to detect anomaly from videos based on sparse reconstruction. Different from the traditional methods, two kinds of dictionaries are employed for anomaly detection with one representing the global dictionary and the other indicating the online one. The global dictionary is first trained on training samples, and then used for the local online dictionary learning and anomaly detection. A novel updating scheme is proposed in the local online dictionary learning for an accurate anomaly detection. Experiments on the public databases show that our method can effectively detect abnormal events in complex scenes.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (NSFC), No. 61402049.

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Authors and Affiliations

  1. The Department of Electronic Engineering, BoHai University, JinZhou, China

    Shifeng Li, Chunxiao Liu & Yuqiang Yang

Authors
  1. Shifeng Li
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  2. Chunxiao Liu
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  3. Yuqiang Yang
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Correspondence to Shifeng Li.

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Li, S., Liu, C. & Yang, Y. Anomaly detection based on sparse coding with two kinds of dictionaries. SIViP 12, 983–989 (2018). https://doi.org/10.1007/s11760-018-1243-7

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  • DOI: https://doi.org/10.1007/s11760-018-1243-7

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