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Novel approach based on topological simplification algorithm optimized with particle swarm optimization

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The movement of people can be considered as the flow of liquid, so we can use the methods employed for the flow of liquid to understand the motion of a crowd. Based on this, we present a novel framework for abnormal behavior detection in crowded scenes. We extract a topological structure from the crowd with the topology simplification algorithm. However, a conventional topology simplification algorithm can not work well if we apply it to the crowd directly because there is too much noises produced by the random motion of the people in the original image. To overcome this, we make a step forward by optimizing this model using Particle Swarm Optimization (PSO) to perform the advection of particle population spread randomly over the image frames. Then we propose two new methods for analyzing the boundary point structure and extraction of a critical point from the particle motion field; both methods can be used to describe the global topological structure of the crowd motion. The advantage of our approach is that each kind of abnormal event can be described as a specific change in the topological structure, so we do not need construct a complex classifier, but can classify the crowd anomalies dynamically and directly. Moreover, the approach monitors the crowd motion macroscopically, making it insensitive to the motion of an individual, disregarding the global movement. The result of an experiment conducted on a common data set shows that our method is both precise and stable.
Rocznik
Strony
115--132
Opis fizyczny
Bibliogr. 18 poz., il., tab., wykr.
Twórcy
autor
  • Cloud Computing, Research Department, ETRI, Daejeon 305-700, Korea
autor
  • Cloud Computing, Research Department, ETRI, Daejeon 305-700, Korea
autor
  • Cloud Computing, Research Department, ETRI, Daejeon 305-700, Korea
autor
  • Cloud Computing, Research Department, ETRI, Daejeon 305-700, Korea
autor
  • Cloud Computing, Research Department, ETRI, Daejeon 305-700, Korea
Bibliografia
  • [1] M. A. Fischler and R. C. Bolles : Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6), 381-395, 1981.
  • [2] B. K. Horn and B. G. Schunck : Determining optical flow. Artificial Intelligence, vol. 17, Issues 1-3, 185-203, 1981.
  • [3] Helman J. and Hesselink L. : Visualizing vector field topology in fluid flows. IEEE Computer Graphics and Applications, 11(3), 36-46, 1991.
  • [4] D. Helbing and P. Molnar. Social force model for pedestrian dynamics :Physical Review E, 51: 4282, 1995.
  • [5] Kennedy, James and Russell Eberhart : Particle swarm optimization. Neural Networks. Proceedings. IEEE International Conference on. Vol. 4, 1995.
  • [6] Kenwright DN : Automatic Detection of open and closed separation and attachment lines[C]. Proc. IEEE Visualization’98, 151-158, 1998.
  • [7] Kenwright DN, Heinzec and LEVIT C. : Feature extraction of separation and attachment lines[J]. IEEE Transactions on Visualization and Computer Graphics, 5(2), 135-144, 1999.
  • [8] Scheuermann G, Hamann B, Joy K I, et al : Visualizing local vector field topology[J]. Journal of Electronic Imaging, 9(4), 356-367, 2000.
  • [9] X. Tricoche, G. Scheuermann and H. Hagen : A topology simplification method for 2d vector fields. In Visualization 2000. Proceedings, 359-366, 2000.
  • [10] Wischgoll T. and Scheuermann G. : Detection and visualization of closed stream lines in planar flows[J]. IEEE Transactions on Visualization and Computer Graphics, 7(2): 165-172, 2001.
  • [11] R. L. Hughes : A continuum theory for the flow of pedestrians. Transportation Research Part B : Methodologica, 36(6), 507-535, 2002.
  • [12] K. D. Xiaoshan Pan, Charles S., Han and K. H. Law : Human and social behavior in computational modeling and analysis of egress. Automation in Construction, 15(4), 448-461, 2006.
  • [13] S. Ali, M. Shah: A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. Computer Vision and Pattern Recognition, CVPR’07, IEEE Conference on, 1-6, 2007.
  • [14] A. Basharat, A. Gritai and M. Shah : Learning object motion patterns for anomaly detection and improved object detection. In Computer Vision and Pattern Recognition. CVPR 2008. IEEE Conference on, 1-8, 2008.
  • [15] J. Kim and K. Grauman : Observe locally, infer globally: A space-time mrf for detecting abnormal activities with incremental updates. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 2921-2928, 2009.
  • [16] Mehran, R., Oyama, A. and Shah, M. : Abnormal crowd behavior detection using social force model. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 935-942, 2009.
  • [17] Garate C., Bilinsky P. and Brernond F.: Crowd event recognition using HOG tracker. In Proceeding of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter). Snowbird, USA, 1-6, 2009.
  • [18] Mahadevan, V., Li, W., Bhalodia, V. and Vasconcelos, N. : Anomaly detection in crowded scenes. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1975-1981, 2010.
Typ dokumentu
Bibliografia
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