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Motion interaction field for detection of abnormal interactions

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Abstract

This paper proposes a new method for the modeling of interactions among objects and for the detection of abnormal interactions in a video. To model interactions among multiple moving objects, we design a motion interaction field (MIF) that is similar to a water waveform generated by multiple objects moving on the surface of water and that describes the intensity of motion interaction in a video. Using the MIF, we establish a framework to detect abnormal interactions, which consists of rule-based decision about regions of interest and dictionary learning-based anomaly decision for these regions. The regions of interest are determined as the regions remaining after filtering out collision-free regions that are recognized clearly to be normal by a rule-based decision based on the shape of MIF. The MIF values in these regions are then used to construct spatiotemporal features for the detection of abnormal interactions by a dictionary learning algorithm with sparse representation. In the experiments, the effectiveness of the proposed method is validated through quantitative and qualitative evaluations with three datasets containing typical abnormal interactions such as car accidents, crowd riots, and uncontrolled fighting.

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Notes

  1. http://www.cse.cuhk.edu.hk/leojia/projects/detectabnormal/index.html.

  2. https://youtu.be/YKciFMjzLqQ.

References

  1. Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)

  2. Blunsden, S., Fisher, R.B.: The BEHAVE video dataset: ground truthed video for multi-person behavior classificationt. Ann. BMVA 4, 1–12 (2010)

    Google Scholar 

  3. Breitenstein, M.D., Grabner, H., Van Gool, L.: Hunting nessie-real-time abnormality detection from webcams. In: International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1243–1250. IEEE (2009)

  4. Brendel, W., Todorovic, S.: Learning spatiotemporal graphs of human activities. In: International Conference on Computer Vision (ICCV), pp. 778–785 (2011)

  5. Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., Schnörr, C.: Variational optical flow computation in real time. IEEE Trans. Image Process. 14(5), 608–615 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  6. Choi, W., Shahid, K., Savarese, S.: Learning context for collective activity recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 3273–3280. IEEE (2011)

  7. Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: Computer Vision and Pattern Recognition (CVPR) (2011)

  8. Cords, H., Staadt, O.G.: Real-time open water environments with interacting objects. Eurographics Workshop on Natural Phenomena (2009)

  9. Cui, X., Liu, Q., Gao, M., Metaxas, D.N.: Abnormal detection using interaction energy potentials. In: Computer Vision and Pattern Recognition (CVPR), pp. 3161–3167. IEEE (2011)

  10. Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: SCIA’03: Proceedings of the 13th Scandinavian Conference on Image Analysis. Linkoping University, Springer (2003)

  11. Helbing, D., Tilch, B.: Generalized force model of traffic dynamics. Phys. Rev. E 58(1), 133–138 (1998)

    Article  Google Scholar 

  12. Hospedales, T., Gong, S., Xiang, T.: A Markov clustering topic model for mining behaviour in video. In: International Conference on Computer Vision (ICCV) (2009)

  13. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory IT–8, 179–187 (1962)

    MATH  Google Scholar 

  14. Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1450–1464 (2006)

    Article  Google Scholar 

  15. Jeong, H., Yoo, Y., Yi, K.M., Choi, J.Y.: Two-stage online inference model for traffic pattern analysis and anomaly detection. Mach. Vis. Appl. 25(6), 1501–1517 (2014)

    Article  Google Scholar 

  16. Konrad, J.: Motion Detection and Estimation. Handbook of Image and Video Processing, 2nd edn. Academic Press, Cambridge (2005)

    Google Scholar 

  17. Lan, T., Wang, Y., Yang, W., Mori, G.: Beyond actions: discriminative models for contextual group activities. In: Advances in Neural Information Processing Systems, pp. 1216–1224 (2010)

  18. Liu, C.: Beyond pixels: exploring new representations and applications for motion analysis. Doctoral thesis, Massachusetts Institute of Technology (2009)

  19. Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: International Conference on Computer Vision (ICCV) (2013)

  20. Marsden, M., McGuinness, K., Little, S., O’Connor, N.E.: Holistic features for real-time crowd behaviour anomaly detection. In: International Conference on Image Processing (ICIP) (2016)

  21. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Computer Vision and Pattern Recognition (CVPR) (2009)

  22. Nam, Y.: Crowd riot dataset. https://sites.google.com/site/yynams/crowd-activity (2013)

  23. Raghavendra, R., Bue, A.D., Cristani, M., Murino, V.: Optimizing interaction force for global anomaly detection in crowded scenes. In: International Conference on Computer Vision Workshops (ICCV Workshops) (2011)

  24. Saleemi, I., Shafique, K., Shah, M.: Probabilistic modeling of scene dynamics for applications in visual surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 31(8), 1472–1485 (2009)

    Article  Google Scholar 

  25. Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. CVPR 12, 2112–2119 (2012)

  26. Sandhan, T., Sethi, A., Srivastava, T., Choi, J.Y.: Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns. In: International Conference on Image and Vision Computing New Zealand, pp. 494–499. IEEE (2013)

  27. Schuster, R., Mörzinger, R., Haas, W., Grabner, H., Van Gool, L.: Real-time detection of unusual regions in image streams. In: Proceedings of the International Conference on Multimedia, pp. 1307–1310 (2010)

  28. Shao, J., Kang, K., Loy, C.C., Wang, X.: Deeply learned attributes for crowded scene understanding. In: Computer Vision and Pattern Recognition (CVPR), pp. 4657–4666. IEEE (2015)

  29. Sultani, W., Choi, J.: Abnormal traffic detection using intelligent driver model. In: International Conference on Pattern Recognition (ICPR) (2010)

  30. UMN: Unusual crowd activity dataset. http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi (2009)

  31. Vahdat, A., Gao, B., Ranjbar, M., Mori, G.: A discriminative key pose sequence model for recognizing human interactions. In: International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1729–1736. IEEE (2011)

  32. Wang, X., Ma, X., Grimson, W.E.L.: Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 539–555 (2009)

    Article  Google Scholar 

  33. Wang, X., Tieu, K., Grimson, E.: Learning semantic scene models by trajectory analysis. In: European Conference on Computer Vision (ECCV), pp. 110–123. 9th European Conference on Computer Vision, Berlin, Heidelberg (2006)

  34. Yun, K., Jeong, H., Yi, K.M., Kim, S.W., Choi, J.Y.: Motion interaction field for accident detection in traffic surveillance video. In: International Conference on Pattern Recognition (ICPR) (2014)

  35. Yun, K., Kim, J., Kim, S.W., Jeong, H., Choi, J.Y.: Learning with adaptive rate for online detection of unusual appearance. In: International Symposium on Visual Computing, pp. 1–10 (2014)

  36. Zhang, Y., Qin, L., Yao, H., Huang, Q.: Abnormal crowd behavior detection based on social attribute-aware force model. In: International Conference on Image Processing (ICIP) (2012)

  37. Zhao, B., Fei-Fei, L., Xing, E.P.: Online detection of unusual events in videos via dynamic sparse coding. In: Computer Vision and Pattern Recognition (CVPR) (2011)

  38. Zhou, B., Tang, X., Zhang, H., Wang, X.: Measuring crowd collectiveness. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1586–1599 (2014)

    Article  Google Scholar 

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Acknowledgements

This work was partly supported by the ICT R&D program of MSIP/IITP [B0101-15-0552, Development of Predictive Visual Intelligence Technology] and the Brain Korea 21 Plus Project in 2016.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, ASRI, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, Korea

    Kimin Yun, Youngjoon Yoo & Jin Young Choi

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  1. Kimin Yun
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  2. Youngjoon Yoo
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  3. Jin Young Choi
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Correspondence to Jin Young Choi.

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Yun, K., Yoo, Y. & Choi, J.Y. Motion interaction field for detection of abnormal interactions. Machine Vision and Applications 28, 157–171 (2017). https://doi.org/10.1007/s00138-016-0816-0

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