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SCIENCE CHINA Information Sciences, Volume 63 , Issue 5 : 154101(2020) https://doi.org/10.1007/s11432-018-9792-8

Anomaly detection by exploiting the tracking trajectory in surveillance videos

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  • ReceivedSep 18, 2018
  • AcceptedFeb 15, 2019
  • PublishedFeb 25, 2020

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61872024, 61472020).


Supplement

Videos and other supplemental documents.


References

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[3] Mehran R, Oyama A, et al. Abnormal crowd behavior detection using social force model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009. 935--942. Google Scholar

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[5] Bae S, Yoon K. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2014. 1218--1225. Google Scholar

[6] Multiple object tracking benchmark. https://motchallenge.net/results/2D_MOT_2015/. Google Scholar

  • Figure 1

    (Color online) (a) Results of the application of proposed method to the UCSD Ped1, UMN, and PKU-SVD-B datasets; (b) anomaly detection using the PKU-SVD-B dataset; (c) comparative results of the tracking trajectory with respect to the 2D MOT 2015 benchmark; (d) the process of double fusion method. A and B represent different video sequences.

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