Welcome to Journal of Beijing Institute of Technology
Advance Search
Volume 28Issue 4
.
Turn off MathJax
Article Contents
Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2019> 28(4): 756-763
Zheyi Fan, Wei Li, Zhonghang He, Zhiwen Liu. Abnormal Crowd Behavior Detection Based on the Entropy of Optical Flow[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(4): 756-763. doi: 10.15918/j.jbit1004-0579.18098
Citation: Zheyi Fan, Wei Li, Zhonghang He, Zhiwen Liu. Abnormal Crowd Behavior Detection Based on the Entropy of Optical Flow[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(4): 756-763.doi:10.15918/j.jbit1004-0579.18098
shu

Abnormal Crowd Behavior Detection Based on the Entropy of Optical Flow

doi:10.15918/j.jbit1004-0579.18098

  • School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2018-06-11
    Abstract
  • To improve the detection accuracy and robustness of crowd anomaly detection, especially crowd emergency evacuation detection, the abnormal crowd behavior detection method is proposed. This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd. First, the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained. Then, the improved optical flow entropy, combining information theory with statistical physics is calculated from 2D optical flow histograms. Finally, the anomaly can be detected according to the abnormality judgment formula. The experimental results show that the detection accuracy achieved over 95% in three public video datasets, which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.
    • FullText(HTML)
  • loading
    • References (21)
  • [1]
    Gao X, Chen Z, Tang S, et al. Adaptive weighted imbalance learning with application to abnormal ac-tivity recognition[J]. Neurocomputing, 2016, 173:1927-1935.
    [2]
    Wang Q, Ma Q, Luo C H, et al. Hybrid histogram of oriented optical flow for abnormal behavior de-tection in crowd scenes[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(2).DOI: 10.1142/S0218001416550077.
    [3]
    Chan Y T, Wang S J, Tsai C H. Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems[J]. Journal of Electronic Imaging, 2017, 26(5):051402.
    [4]
    Liu J J, Tao H W, Luo L, et al. Video anomaly detection algorithm combined with histogram of ori-ented gradients and optical flow[J]. Journal of Signal Processing, 2016, 32(1):1-7.
    [5]
    Khan S D, Bandini S, Basalamah S, et al. Analyzing crowd behavior in naturalistic conditions[J]. Neu-rocomputing, 2016, 177(C):543-563.
    [6]
    Mehran R, Oyama A, Shan M. Abnormal crowd behavior detection using social force model[C]//Computer Vision and Pattern Recognition. Miami, FL, USA:IEEE, 2009:935-942.
    [7]
    Cong Y, Yuan J, Liu J. Sparse reconstruction cost for abnormal event detection[C]//Computer Vi-sion and Pattern Recognition. Salt Lake City, USA:IEEE, 2011:3449-3456.
    [8]
    Bagheri M A, Gao Q, Escalera S, et al. Locality regularized group sparse coding for action recogni-tion[J]. Computer Vision and Image Understanding, 2017, 158(C):106-114.
    [9]
    Jiang J, Wu X H, He X H, et al. Measuring crowd collectiveness via compressive sensing[J]. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2015, 98(11):2263-2266.
    [10]
    Horn B K P, Schunck B G. Determining optical flow[J]. Artificial Intelligence, 1981, 17(1-3):185-203.
    [11]
    Hao Y, Xu Z, Wang J, et al. An approach to detect crowd panic behavior using flow-based feature[C]//International Conference on Automation and Computing. Colchester, UK:IEEE, 2016:462-466.
    [12]
    Krausz B, Bauckhage C. Loveparade 2010:Automatic video analysis of a crowd disaster[J]. Com-puter Vision & Image Understanding, 2012, 116(3):307-319.
    [13]
    Mahadevan V, Li W, Bhalodia V, et al. Anomaly detection in crowded scenes[C]//Computer Vision and Pattern Recognition. San Francisco, USA:IEEE, 2010:1975-1981.
    [14]
    Wu S, Moore B E, Shah M. Chaotic invariants of Lagrangian particle trajectories for anomaly detec-tion in crowded scenes[C]//Computer Vision and Pattern Recognition. San Francisco, USA:IEEE, 2010:2054-2060.
    [15]
    Wu S, Yang H, Zheng S, et al. Crowd behavior analysis via curl and divergence of motion trajecto-ries[J]. International Journal of Computer Vision, 2017, 123(3):1-21.
    [16]
    Ye R, Li X. Collective representation for abnormal event detection[J]. Journal of Computer Science & Technology, 2017, 32(3):470-479.
    [17]
    Feng Y, Yuan Y, Lu X. Learning deep event models for crowd anomaly detection[J]. Neurocomputing, 2016, 219:548-556.
    [18]
    Liu C. Beyond pixels:Exploring new representations and applications for motion analysis[D]. Boston, USA:Massachusetts Institute of Technology, 2009.
    [19]
    Cover T M, Thomas M, Zhang H, et al. Elements of information theory[M].Beijing:China Machine Press, 2005:7-11. (in Chinese)
    [20]
    University of Minnesota. UMN unusual crowd activity dataset[EB/OL].[2018-03-12]. http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi.
    [21]
    Susan S, Hanmandlu M. Unsupervised detection of nonlinearity in motion using weighted average of non-extensive entropies[J]. Signal, Image and Video Processing, 2015, 9(3):511-525.
    • Relative Articles
  • Supplements (0)
  • Cited By
  • 加载中

Catalog

    通讯作者:陈斌, bchen63@163.com
    • 1.

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (450) PDF downloads(239) Cited by()
    Proportional views
    Related

    Address:5 Zhongguancun South Street, Haidian District, Beijing

    Tel:86-10-68914374

    Email:blgywb@bit.edu.cn

    Copy Right © Journal of Beijing Institute of Technology

    Supported by:Beijing Renhe Information Technology Co. Ltd

    /

      Return
      Return
        Baidu
        map