Detecting dominant motions in dense crowds

IEEE Journal on Selected Topics in Signal Processing

Volumn 2, Issue 4, 2008, Pages 568-581

  Cheriyadat, Anil M ^a   Radke, Richard J ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

Anomaly detection; Clustering; Crowd analysis; Longest common subsequences; Object trajectories

Indexed keywords

COMPUTER VISION; IMAGE PROCESSING; NETWORK PROTOCOLS; OBJECT RECOGNITION; PHOTOGRAPHY; SENSOR NETWORKS; TRAJECTORIES; VIDEO RECORDING;

ANOMALY DETECTION; CLUSTERING; CROWD ANALYSIS; LONGEST COMMON SUBSEQUENCES; OBJECT TRAJECTORIES;

OPTICAL FLOWS;

EID: 54049140952     PISSN: 19324553     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSTSP.2008.2001306     Document Type: Article

References (53)

1. W. Buchanan, "Photos show 65 000 at peak of S.F. Rally; Aerial study casts doubt on estimates of 200 000," The San Francisco Chronicle Feb. 21, 2003.
2. M. Levenson, "Crowd size could be in the eye ofbeholder," The Boston Globe, Nov. 1, 2004.
3. D. Howell, "Dissatisfaction on the marches," The Washington Post Feb. 4, 2007.
4. A. Cheriyadat and R. Radke, "Automatically determining dominant motions in crowded scenes by clustering partial feature trajectories," in Proc. 1st ACM/IEEE Int. Conf. Distributed Smart Cameras (ICDSC-07), Sep. 2007, pp. 52-58.
5. C. R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Real-time tracking of the human body," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, no. 7, pp. 780-785, Jul. 1997.
6. M. S. Allili and D. Ziou, "Object contour tracking in videos by matching finite mixture models," in Proc. IEEE Conf. Video Signal Based Surveillance, Nov. 2006, p. 35.
7. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 5, pp. 603-619, May 2002.
8. G. J. Brostow and R. Cipolla, "Unsupervised Bayesian detection of independent motion in crowds," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2006, vol. 1, pp. 594-601.
9. V. Rabaud and S. Belongie, "Counting crowded moving objects," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2006, vol. 1, pp.705-711.
10. D. M. Gavrila, "The visual analysis of human movement: A survey," Comput. Vision Image Understanding, vol. 73, no. 1, pp. 82-98, 1999.
11. G. K. Still, "Crowd Dynamics," Ph.D. dissertation, Univ. Warwick, Warwick, U.K., 2000.
12. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and practice of background maintenance," in Proc. ICCV'99,1999, pp.255-261.
13. R. Collins, A. Lipton, and T. Kanade, "Introduction to the special section on video surveillance," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 8, pp. 745-746, Aug. 2000.
14. R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image change detection algorithms: A systematic survey," IEEE Trans. Image Processing, vol. 14, no. 3, pp. 294-307, Mar. 2005.
15. C. Stauffer and W. E. L. Grimson, "Learning patterns of activity using real-time tracking," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 8, pp. 747-757, Aug. 2000.
16. A. Elgammal, R. Duraiswami D. Harwood, and L. S. Davis, "Background and foreground modeling using non-parametric kernel density estimation for visual surveillance," Proc. IEEE, vol. 90, no. 7, pp. 1151-1163, Jul. 2002.
17. I. Haritaoglu, D. Harwood, and L. S. Davis, "W4: Real-time surveillance of people and their activities," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 8, pp. 809-830, Aug. 2000.
18. M. Isard and J. MacCormick, "BraMBLe: A bayesian multiple-blob tracker," in Proc. Int. Conf. Comput. Vision, 2001, pp. 34-41.
19. T. Zhao and R. Nevatia, "Tracking multiple humans in crowded environment," in Proc. IEEE Conf. Comput. Vision Pattern Recognition 2004, pp. 406-413.
20. Z. Khan, T. Balch, and F. Dellaert, "An MCMC-based particle filter for tracking multiple interacting targets," IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 11, pp. 1805-1819, Nov. 2004.
21. T. Zhao and R. Nevatia, "Stochastic human segmentation from a static camera," in Proc. IEEE Workshop Motion Video Comput., 2002, p. 9.
22. B. Wu and R. Nevatia, "Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors," Int. J. Comput. Vision, pp. 1-20, 2007.
23. S. M. Khan and M. Shah, "A multiview approach to tracking people in crowded scenes using a planar homography constraint," in Proc. Eur. Conf. Comput. Vision, 2006, pp. 1-14.
24. C. Harris and M. Stephens, "A combined corner and edge detector," in Proc. 4th Alvey Vision Conf., 1988, pp. 147-151.
25. P. Tu and J. Rittscher, "Crowd segmentation through emergent labeling," in Proc. ECCV Workshop Statistical Methods Video Processing, 2004, pp. 187-198.
26. J. Rittscher, P. Tu, and N. Krahnst, "Simultaneous estimation of segmentation and shape," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2005, pp. 486-493.
27. B. Leibe, E. Seemann, and B. Schiele, "Pedestrian detection in crowded scenes," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2005, pp. 878-885.
28. M. D. Rodriguez and M. Shah, "Detecting and segmenting humans in crowded scenes," in Proc. ACM Multimedia, 2007, pp. 353-356.
29. G. Mori, S. Belongie, and J. Malik, "Efficient shape matching using shape contexts," IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 11, pp. 1832-1837, Nov. 2005.
30. P. Reisman, O. Mano, S. Avidan, and A. Shashua, "Crowd detection in video sequences," in Proc. IEEE Intelligent Vehicles Symp., 2004, pp. 1-6.
31. A. Davies, J. H. Yin, and S. Velastin, "Crowd monitoring using image processing," Electron. Commun. Eng. J., vol. 7, no. 1, pp. 37-47, Feb. 1995.
32. A. Marana, S. A. Velastin, L. F. Costa, and R. A. Lotufo, "Automatic estimation of crowd density using texture," Safety Sci., vol. 28, no. 3, pp. 165-175, Apr. 1998.
33. E. Andrade, S. Blunsden, and R. Fisher, "Modelling crowd scenes for event detection," in Proc. l8th Int. Conf. Pattern Recognition, 2006, vol. 1, pp. 175-178.
34. E. Andrade and R. Fisher, "Simulation of crowd problems for computer vision," in Proc. 1st Int. Workshop Crowd Simulation, 2005, pp. 71-80.
35. W. Yan and D. A. Forsyth, "Learning the behavior of users in a public space through video tracking," in Proc. IEEE Workshop Appl. Comput. Vision, 2005, vol. 1, pp. 370-377.
36. B. Zhan, P. Remagnino, and S. Velastin, "Mining paths of complex crowd scenes," in Proc. 1st Int. Symp. Adv. Visual Comput. (ISVC , Dec. 2005, pp. 126-133.
37. S. Ali and M. Shah, "A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, Jun. 2007, pp. 1-6.
38. S. Khalid and A. Naftel, "Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space," Multimedia Syst., vol. 12, no. 3, pp. 227-238, 2006.
39. G. Antonini and J. P. Thiran, "Counting pedestrians in video sequences using trajectory clustering," IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 8, pp. 1008-1020, Aug. 2006.
40. I. N. Junejo, O. Javed, and M. Shah, "Multi feature path modeling for video surveillance," in Proc. l7th Int. Conf. Pattern Recognition 2004, vol. 2, pp. 716-719.
41. J. Alon, S. Sclaroff, G. Kollios, and V. Pavlovic, "Discovering clusters in motion time-series data," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, Jun. 2003, pp. 375-381.
42. C. Piciarelli and G. L. Foresti, "On-line trajectory clustering for anomalous events detection.," Pattern Recognition Lett., vol. 27, no. 15, pp. 1835-1842, 2006.
43. B. Yi, H. Jagadish, and C. Faloutsos, "Efficient retrieval of similar time sequences under time warping," in Proc. Int. Conf. Data Eng. 1998, pp. 201-208.
44. D. Buzan, S. Sclaroff, and G. Kollios, "Extraction and clustering of motion trajectories in video," in Proc. Int. Conf. Pattern Recognition, 2004, pp. 521-524.
45. M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E. Keogh, "Indexing multidimensional time-series with support for multiple distance measure," in Proc. SIGKDD, 2003, pp. 216-225.
46. J. Shi and C. Tomasi, "Good features to track," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 1994, pp. 593-600.
47. E. Rosten and T. Drummond, "Machine learning for high speed corner detection," in Proc. Eur. Conf. Comput. Vision, 2006, pp. 430-433.
48. G. Bradski, "OpenCV: Examples of use and new applications in stereo, recognition and tracking," in Proc. Int. Conf. Vision Interface, 2002 [Online]. Available: http://www.intel.com/ technology/computing/opencv/
49. B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in Proc. 7th Int. Joint Conf. Artificial Intell., 1981, pp. 674-679.
50. UK EPSRC REASON Project Consortium, "PETS 2007 Benchmark Data," in Proc. IEEE Workshop Performance Evaluation Tracking Surveillance, 2007 [Online]. Available: http://www.cvg.rdg.ac.uk/ PETS2007/data.html
51. P. Sand and S. Teller, "Particle video: Long range motion estimation using point trajectories," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2006, pp. 1-8.
52. A. Buchanan and A. Fitzgibbon, "Combining local and global motion models for feature point tracking," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2007, pp. 1-8.
53. A. Cohen, C. Bjornsson, Y. Chen, G. Banker, E. Ladi, E. Robey, S. Temple, and B. Roysam, "Automatic summarization of changes in image sequences using algorithmic information theory," in Proc. IEEE Int. Symp. Biomed. Imag., May 2008, pp. 859-862.