Understanding pedestrian behaviors from stationary crowd groups

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 488-496

  Yi, Shuai ^a   Li, Hongsheng ^a,b   Wang, Xiaogang ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LARGE DATASET; SECURITY SYSTEMS;

ABNORMAL EVENT DETECTIONS; CROWD SURVEILLANCE; MULTIPLE APPLICATIONS; PEDESTRIAN BEHAVIOR; PEDESTRIAN BEHAVIOR MODEL; SCENE UNDERSTANDING; VIDEO SURVEILLANCE; WALKING PATTERN;

BEHAVIORAL RESEARCH;

EID: 84959223987     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298971     Document Type: Conference Paper

References (45)

1. A. Alahi, V. Ramanathan, and L. Fei-Fei. Socially-aware large-scale crowd forecasting. In Proc. CVPR. IEEE, 2014
2. S. Ali and M. Shah. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In Proc. CVPR. IEEE, 2007
3. S. Ali and M. Shah. Floor fields for tracking in high density crowd scenes. In Proc. ECCV. Springer, 2008
4. G. Antonini, S. V. Martinez, M. Bierlaire, and J. P. Thiran. Behavioral priors for detection and tracking of pedestrians in video sequences. International Journal of Computer Vision, 69(2):159-180, 2006
5. E. Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America, 99(Suppl 3):7280-7287, 2002
6. M.-C. Chang, N. Krahnstoever, and W. Ge. Probabilistic group-level motion analysis and scenario recognition. In Proc. ICCV. IEEE, 2011
7. R. Emonet, J. Varadarajan, and J.-M. Odobez. Extracting and locating temporal motifs in video scenes using a hierarchical non parametric Bayesian model. In Proc. CVPR. IEEE, 2011
8. D. Forsyth. Group dynamics. Cengage Learning, 2009
9. W. Ge, R. T. Collins, and R. B. Ruback. Vision-based analysis of small groups in pedestrian crowds. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(5):1003-1016, 2012
10. S. J. Guy, S. Kim, M. C. Lin, and D. Manocha. Simulating heterogeneous crowd behaviors using personality trait theory. In Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. ACM, 2011
11. D. Helbing, I. Farkas, and T. Vicsek. Simulating dynamical features of escape panic. Nature, 407(6803):487-490, 2000
12. D. Helbing and P. Molnar. Social force model for pedestrian dynamics. Physical review E, 51(5):4282, 1995
13. T. Hospedales, S. Gong, and T. Xiang. A markov clustering topic model for mining behaviour in video. In Proc. ICCV. IEEE, 2009
14. T. M. Hospedales, J. Li, S. Gong, and T. Xiang. Identifying rare and subtle behaviors: A weakly supervised joint topic model. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(12):2451-2464, 2011
15. W. Hu, D. Xie, Z. Fu, W. Zeng, and S. Maybank. Semantic-based surveillance video retrieval. Image Processing, IEEE Transactions on, 16(4):1168-1181, 2007
16. K. Kim, D. Lee, and I. Essa. Gaussian process regression flow for analysis of motion trajectories. In Proc. ICCV. IEEE, 2011
17. R. Kimmel, A. Amir, and A. M. Bruckstein. Finding shortest paths on surfaces using level sets propagation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 17(6):635-640, 1995
18. D. Kuettel, M. D. Breitenstein, L. Van Gool, and V. Ferrari. What's going on? Discovering spatio-temporal dependencies in dynamic scenes. In Proc. CVPR. IEEE, 2010
19. T. Lan, L. Sigal, and G. Mori. Social roles in hierarchical models for human activity recognition. In Proc. CVPR. IEEE, 2012
20. T. Lan, Y. Wang, W. Yang, S. N. Robinovitch, and G. Mori. Discriminative latent models for recognizing contextual group activities. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(8):1549-1562, 2012
21. G. Le Bon. The crowd: A study of the popular mind. Macmillian, 1897
22. D. Lin, E. Grimson, and J. Fisher. Learning visual flows: A lie algebraic approach. In Proc. CVPR. IEEE, 2009
23. D. Lin, E. Grimson, and J. Fisher. Modeling and estimating persistent motion with geometric flows. In Proc. CVPR. IEEE, 2010
24. D. Makris and T. Ellis. Learning semantic scene models from observing activity in visual surveillance. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 35(3):397-408, 2005
25. R. Mehran, A. Oyama, and M. Shah. Abnormal crowd behavior detection using social force model. In Proc. CVPR. IEEE, 2009
26. B. T. Morris and M. M. Trivedi. Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(11):2287-2301, 2011
27. M. Moussäd, D. Helbing, and G. Theraulaz. How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences, 108(17):6884-6888, 2011
28. M. Moussäd, N. Perozo, S. Garnier, D. Helbing, and G. Theraulaz. The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PloS one, 5(4):e10047, 2010
29. S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool. You'll never walk alone: Modeling social behavior for multi-target tracking. In Proc. ICCV. IEEE, 2009
30. L. A. Pervin. The science of personality. Oxford University Press, 2003
31. P. Scovanner and M. F. Tappen. Learning pedestrian dynamics from the real world. In Proc. ICCV. IEEE, 2009
32. J. A. Sethian. A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of Sciences, 93(4):1591-1595, 1996
33. J. Shao, K. Kang, C. C. Loy, and X. Wang. Deeply learned attributes for crowded scene understanding. In Proc. CVPR. IEEE, 2015
34. J. Shao, C. C. Loy, and X. Wang. Scene independent group profiling in crowd. In Proc. CVPR. IEEE, 2014
35. G. Shu, A. Dehghan, and M. Shah. Improving an object detector and extracting regions using superpixels. In Proc. CVPR. IEEE, 2013
36. X. Wang, K. T. Ma, G.-W. Ng, and W. E. L. Grimson. Trajectory analysis and semantic region modeling using nonparametric hierarchical Bayesian models. International Journal of Computer Vision, 95(3):287-312, 2011
37. X. Wang, X. Ma, and W. E. L. Grimson. Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(3):539-555, 2009
38. S. Yi and X. Wang. Profiling stationary crowd groups. In Proc. ICME. IEEE, 2014
39. S. Yi, X. Wang, C. Lu, and J. Jia. L0 regularized stationary time estimation for crowd group analysis. In Proc. CVPR. IEEE, 2014
40. C. Zhang, H. Li, X. Wang, and X. Yang. Cross-scene crowd counting via deep convolutional neural networks. In Proc. CVPR. IEEE, 2015
41. B. Zhou, X. Tang, and X. Wang. Measuring crowd collectiveness. In Proc. CVPR. IEEE, 2013
42. B. Zhou, X. Tang, and X. Wang. Learning collective crowd behaviors with dynamic pedestrian-agents. International Journal of Computer Vision, 111(1):50-68, 2015
43. B. Zhou, X. Tang, H. Zhang, and X. Wang. Measuring crowd collectiveness. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(8):1586-1599, 2014
44. B. Zhou, X. Wang, and X. Tang. Random field topic model for semantic region analysis in crowded scenes from tracklets. In Proc. CVPR. IEEE, 2011
45. B. Zhou, X. Wang, and X. Tang. Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents. In Proc. CVPR. IEEE, 2012.