Towards generic detection of unusual events in video surveillance

6th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009

Volumn , Issue , 2009, Pages 61-66

  Ivanov, Ivan ^a   Dufaux, Frederic ^a   Ha, Thien M ^a   Ebrahimi, Touradj ^a  

^a EPFL   (Switzerland)

Author keywords

Feature extraction; Support vector machine classifier; Trajectory representation; Unusual event; Video surveillance application

Indexed keywords

AUTOMATIC DETECTION; EVENT DETECTION IN VIDEO; MOVING OBJECTS; PRE-PROCESSING; SUPPORT VECTOR MACHINE METHOD; TRAINING SEQUENCES; TYPICAL SEQUENCES; VIDEO SURVEILLANCE; VIDEO SURVEILLANCE APPLICATION; VIDEO-SURVEILLANCE APPLICATIONS;

CLASSIFIERS; FEATURE EXTRACTION; IMAGE RETRIEVAL; MONITORING; SIGNAL DETECTION; SUPPORT VECTOR MACHINES; TRACKING (POSITION); VIDEO AMPLIFIERS;

SECURITY SYSTEMS;

EID: 72349092005     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AVSS.2009.63     Document Type: Conference Paper

References (15)

1. W. Hu, T. Tan, L. Wang and S. Maybank, "A survey on visual surveillance of object motion and behaviors," IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 34(3):334-352, 2004.
2. F. I. Bashir, A. A. Khokhar and D. Schonfeld, "Segmented trajectory based indexing and retrieval of video data," Proc. of IEEE Int. Conf. on Image Processing, pp. 623-626, 2003.
3. G. Antonini and J.P. Thiran, "Counting Pedestrians in Video Sequences Using Trajectory Clustering," IEEE Trans. on Circuits and Systems for Video Technology, 16(8):1008-1020, 2006.
4. F. Porikli and T. Haga, "Event Detection by Eigenvector Decomposition using Object and Frame Features," Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 114-123, 2004.
5. A. Prati, S. Calderara and R. Cucchiara, "Using Circular Statistics for Trajectory Analysis," Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
6. Z. Zhang, K. Huang and T. Tan, "Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes," Proc. of IEEE Int. Conf. on Pattern Recognition, pp. 1135-1138, 2006.
7. I. N. Junejo, O. Javed and M. Shah, "Multi feature path modeling for video surveillance," Proc. of IEEE Int. Conf. on Pattern Recognition, pp. 716-719, 2004.
8. S. Calderara, R. Cucchiara and A. Prati, "Dynamic Programming Technique for Classifying Trajectories," Proc. of IEEE Int. Conf. on Image Analysis and Processing, pp. 137-142, 2007.
9. Z. Fu, W. Hu and T. Tan, "Similarity based vehicle trajectory clustering and anomaly detection," Proc. of IEEE Int. Conf. on Image Processing, pp.602-605, 2005.
10. N. Anjum and A. Cavallaro, "Object Trajectory Clustering for Video Analysis," IEEE Trans. On Circuits and Systems for Video Technology, 18(11):1555-1564, 2008.
11. A. Cavallaro, O. Steiger and T. Ebrahimi, "Tracking video objects in cluttered background," IEEE Trans. on Circuits and Systems for Video Technology, 15(4):575-584, 2005.
12. C. Flanagan, "The Bresenham Line-Drawing Algorithm," http://www.cs.helsinki.fi/group/goa/mallinnus/lines/bresenh.html, Cited Feb 2009.
13. J. Luo, K. Ying, P. He and J. Bai, "Properties of Savitzky-Golay digital differentiators," Digital Signal Processing, 15(2):122-136, 2005.
14. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, 2:121-167, 1998.
15. C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.