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Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes

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  • Ang Li
  • Zhenjiang Miao
  • Yigang Cen
  • Tian Wang
  • Viacheslav Voronin

Abstract

Abnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect abnormal events in crowded scenes. Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. Compared with other methods based on the optical flow, experiments on several benchmark datasets show that our algorithm is effective with satisfying results.

Suggested Citation

  • Ang Li & Zhenjiang Miao & Yigang Cen & Tian Wang & Viacheslav Voronin, 2015. "Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes," International Journal of Distributed Sensor Networks, , vol. 11(11), pages 406941-4069, November.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:11:p:406941
    DOI: 10.1155/2015/406941
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