Optical Flow Approach Followed by SVM Classification Model to Recognize Abnormal Behavior of a Crowd

  • Authors

    • Basavaraj G.M
    • Dr Ashok Kusagur
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.18927
  • Optical flow descriptor, Motion map, Harris corner detector, SVM classifier, ROC curve

  • Abstract

    A many of researches have been carried out in the field of the crowd behavior recognition system. Recognizing crowd behavior in videos is most challenging and occlusions because of irregular human movement. This paper gives an overview of optical flow model along with the SVM (Support Vector Machine) classification model. This proposed approach evaluates sudden changes in motion of an event and classifies that event to a category: Normal and Abnormal.  Geometric means of location, direction, and displacement of the feature points of each frame are estimated. Harris corner Detector is used in each frame for tracking a set of feature points. Proposed approach is very effective in real time scenario like public places where security is most important. After analyzing result ROC curve (receiver operating characteristics) is plotted which gives classification accuracy. We also presented frame level comparison with Ground truth and social force model (SFM) techniques. Our proposed approach is giving a promising result compare to all state of art methods.

     

     

  • References

    1. [1] S. Ali and M. Shah, “A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis,†in CVPR 2007. IEEE, 2007, pp. 1–6.

      [2] S. Ali and M. Shah, “Floor fields for tracking in high density crowd scenes,†ECCV 2008, pp. 1–14, 2008.

      [3] N. Ihaddadene and C. Djeraba, “Real-time crowd motion analysis,†in ICPR 2008. IEEE, 2008, pp. 1–4.

      [4] W. Ge, R.T. Collins, and B. Ruback, “Automatically detecting the small group structure of a crowd,†in WACV 2009. IEEE, 2009, pp. 1–8.

      [5] R. Mehran, B. Moore, and M. Shah, “A streakline representation of flow in crowded scenes,†ECCV 2010, pp. 439–452, 2010.

      [6] S. Wu, O. Oreifej, and M. Shah, “Action recognition in videos acquired by a moving camera using motion decomposition of lagrangian particle trajectories,†in ICCV 2011. IEEE, 2011, pp. 1419–1426.

      [7] Cui, Q. Liu, M. Gao, and D.N. Metaxas, “Abnormal detection using interaction energy potentials,†in CVPR 2011. IEEE, 2011, pp. 3161–3167.

      [8] Amit Adam, Ehud Rivlin, IlanShimshoni, and DavivReinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Transactionson Pattern Analysis and Machine Intelligence, 30(3):555 560, 2008.

      [9] V. Mahadevan, Weixin Li, V. Bhalodia, and N. Vasconcelos. Anomaly detection in crowded scenes. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 1975 – 1981, 2010.

      [10] L. Kratz and K. Nishino. Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 693 – 700, 2010.

      [11] R. Mehran, A. Oyama, and M. Shah. Abnormal crowd behavior detection using social force model. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 935 – 942, 2009.

      [12] S.Ali and M. Shah. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 1 – 6, 2007.

      [13] L. Li, W. Huang, I. Y. H. Gu, and Q. Tian. Foreground object detection from videos containing complex background. In MULTIMEDIA ’03: Proceedings of the eleventh ACM international conference on Multimedia, pages 2–10, New York, NY, USA, 2003. ACM.

      [14] C. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, page 147-152, 1988.

      [15] S. Wu, B. E. Moore, and M. Shah, “Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes,†in IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2010, pp. 2054–2060.

      [16] R. Mehran, A. Oyama, and M. Shah, “Abnormal crowd behavior detection using social force model,†in IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2009, pp. 935–942.

      [17] Y. Cong, J. Yuan, and J. Liu, “Sparse reconstruction cost for abnormal event detection.†in Computer Vision and Pattern Recognition, CVPR, 2011, pp. 3449–3456.

      [18] V. Saligrama and Z. Chen, “Video anomaly detection based on local statistical aggregates,†in IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2012, pp. 2112–2119.

      [19] W. Li, V. Mahadevan, and N. Vasconcelos, “Anomaly detection and localization in crowded scenes,†IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 36, no. 1, pp. 18–32, 2014.

      [20] Lloyd, Kaelon, et al. "Detecting violent and abnormal crowd activity using temporal analysis of gray level co-occurrence matrix (GLCM)-based texture measures." Machine Vision and Applications 28.3-4 (2017): 361-371.

  • Downloads

  • How to Cite

    G.M, B., & Ashok Kusagur, D. (2018). Optical Flow Approach Followed by SVM Classification Model to Recognize Abnormal Behavior of a Crowd. International Journal of Engineering & Technology, 7(3.34), 156-159. https://doi.org/10.14419/ijet.v7i3.34.18927

    Received date: 2018-09-04

    Accepted date: 2018-09-04

    Published date: 2018-09-01