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Crowd anomaly detection and localization using histogram of magnitude and momentum

Abstract

Video anomaly detection is an important and crucial area from security point of view. Anomaly detection means identifying unusual activities. It is a tedious task to recognize abnormal activities due to its infrequent occurrence in the crowd. Surveillance cameras are installed in crowded places, but manual analysis of video data gathered from these cameras is a cumbersome process and becomes almost impossible if cameras are in large number. In this work, an automated approach is proposed to detect and locate anomalies. A concept of momentum from Physics is used to connect foreground occupancy and motion of object. The proposed work is divided into three major steps: (a) background removal, (b) feature extraction and behavior recognition, and (c) anomaly detection and localization. The background removal step separates the background from each frame. To detect anomalies, appearance and motion characteristics of foreground objects are incorporated by histogram of magnitude and momentum features. Behavior of objects is learned through unsupervised clustering technique. In order to locate anomalies, positional features are used. The proposed approach is verified on benchmark datasets like UCSD and UMN devised for anomaly detection and crowd analysis. Experimental results are validated to contemporary methods.

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References

  1. 1.

    Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 555–560 (2008)

  2. 2.

    Amraee, S., Vafaei, A., Jamshidi, K., Adibi, P.: Anomaly detection and localization in crowded scenes using connected component analysis. Multimed. Tools Appl. 77(12), 14767–14782 (2018)

  3. 3.

    Biswas, S., Babu, R.: Sparse representation based anomaly detection with enhanced local dictionaries. In: IEEE International Conference on Image Processing (ICIP), pp. 5532–5536 (2014)

  4. 4.

    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15(1–58) (2009)

  5. 5.

    Cong, Y., Yuan, J., Liu, J.: Abnormal event detection in crowded scenes using sparse representation. Pattern Recognit. 46(7), 1851–1864 (2013)

  6. 6.

    Cong, Y., Yuan, J., Tang, Y.: Video anomaly search in crowded scenes via spatio-temporal motion context. IEEE Trans. Inf. Forensics Secur. 8(10), 1590–1599 (2013)

  7. 7.

    Dawn, D., Shaikh, S.: A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector. Vis. Comput. 32(3), 289–306 (2016)

  8. 8.

    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2012)

  9. 9.

    Fablet, R., Black, M.: Automatic detection and tracking of human motion with a view-based representation. In: European Conference on Computer Vision (ECCV), pp. 476–491 (2002)

  10. 10.

    Horn, B., Schunck, B.: Determining optical flow. Artif. Intell. 17(1–3), 185–203 (1981)

  11. 11.

    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. 34(3), 334–352 (2004)

  12. 12.

    Jiang, F., Wu, Y., Katsaggelos, A.: Detecting contextual anomalies of crowd motion in surveillance video. In: IEEE International Conference on Image Processing (ICIP), pp. 1117–1120 (2009)

  13. 13.

    Kaltsa, V., Briassouli, A., Kompatsiaris, I., Hadjileontiadis, L.J., Strintzis, M.G.: Swarm intelligence for detecting interesting events in crowded environments. IEEE Trans. Image Process. 24(7), 2153–2166 (2015)

  14. 14.

    Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2928 (2009)

  15. 15.

    Kumar, D., Bezdek, J., Rajasegarar, S., Leckie, C., Palaniswami, M.: A visual-numeric approach to clustering and anomaly detection for trajectory data. Vis. Comput. 33, 265–281 (2017)

  16. 16.

    Lee, D., Suk, H., Park, S., Lee, S.: Motion influence map for unusual human activity detection and localization in crowded scenes. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1612–1623 (2015)

  17. 17.

    Leyva, R., Sanchez, V., Li, C.: Video anomaly detection with compact feature sets for online performance. IEEE Trans. Image Process. 26(7), 3463–3478 (2017)

  18. 18.

    Li, N., Wu, X., Xu, D., Guo, H., Feng, W.: Spatio-temporal context analysis within video volumes for anomalous-event detection and localization. Neurocomputing 155, 309–319 (2015)

  19. 19.

    Li, S., Yang, Y., Liu, C.: Anomaly detection based on two global grid motion templates. Signal Process. Image Commun. 60, 6–12 (2018)

  20. 20.

    Li, T., Chang, H., Wang, M., Ni, B., Hong, R., Yan, S.: Crowded scene analysis: a survey. IEEE Trans. Circuits Syst. Video Technol. 25(3), 367–386 (2015)

  21. 21.

    Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2014)

  22. 22.

    Lin, H., Deng, J.D., Woodford, B.J.: Anomaly detection in crowd scenes via online adaptive one-class support vector machines. In: IEEE International Conference on Image Processing (ICIP), pp. 2434–2438 (2015)

  23. 23.

    Liu, C.: Beyond pixels: exploring new representations and applications for motion analysis. Ph.D. Thesis, Massachusetts Institute of Technology (2009)

  24. 24.

    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1975–1981 (2010)

  25. 25.

    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 935–942 (2009)

  26. 26.

    Reinders, F., Post, F., Spoelder, H.: Visualization of time-dependent data with feature tracking and event detection. Vis. Comput. 17(1), 55–71 (2001)

  27. 27.

    Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2112–2119 (2012)

  28. 28.

    Unusual Crowd Activity Dataset, http://mha.cs.umn.edu/movies/crowdactivity-all.avi/

  29. 29.

    Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behavior understanding in video surveillance. Vis. Comput. 29, 983–1009 (2013)

  30. 30.

    Wang, B., Ye, M., Li, X., Zhao, F., Ding, J.: Abnormal crowd behavior detection using high-frequency and spatio-temporal features. Mach. Vis. Appl. 23(3), 501–511 (2012)

  31. 31.

    Wu, S., Moore, B., Shah, M.: Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2054–2060 (2010)

  32. 32.

    Xiong, G., Wu, X., Chen, Y., Ou, Y.: Abnormal crowd behavior detection based on the energy model. In: IEEE International Conference on Information and Automation (ICIA), pp. 495–500 (2011)

  33. 33.

    Xu, D., Song, R., Wu, X., Li, N., Feng, W., Qian, H.: Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts. Neurocomputing 143, 144–152 (2014)

  34. 34.

    Zhang, T., Wiliem, A., Lovell, B.: Region-based anomaly localization in crowded scenes via trajectory analysis and path prediction. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–7 (2013)

  35. 35.

    Zhang, Y., Qin, L., Ji, R., Yao, H., Huang, Q.: Social attribute-aware force model: exploiting richness of interaction for abnormal crowd detection. IEEE Trans. Circuits Syst. Video Technol. 25(7), 1231–1245 (2015)

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Acknowledgements

The authors acknowledge Computer Vision and Pattern Recognition (CVPR) Laboratory established at Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India, for providing research facilities.

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Correspondence to Suprit D. Bansod.

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The authors declare that no human participants and/or animals are involved in carrying out this work.

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Cite this article

Bansod, S.D., Nandedkar, A.V. Crowd anomaly detection and localization using histogram of magnitude and momentum. Vis Comput 36, 609–620 (2020). https://doi.org/10.1007/s00371-019-01647-0

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Keywords

  • Anomaly detection
  • Surveillance
  • Histogram of magnitude
  • Momentum
  • Crowd analysis