Instantly telling what happens in a video sequence using simple features

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

Volumn , Issue , 2011, Pages 3257-3264

  Wang, Liang ^a,b   Wang, Yizhou ^b,c   Jiang, Tingting ^b,c   Gao, Wen ^b,c  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b PEKING UNIVERSITY   (China)

^c Peking University   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RECOGNITION; MINING;

ACTION RECOGNITION; ANOMALY DETECTION; COMPUTATIONALLY EFFICIENT; CONTRAST SET MINING; DISCRIMINATIVE POWER; EMERGING PATTERNS; EVENT DETECTION; IMPORTANT FEATURES;

VIDEO RECORDING;

EID: 80052881652     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995377     Document Type: Conference Paper

References (19)

1. http://www.itl.nist.gov/iad/mig/tests/trecvid/2009/.
2. http://www.cvg.rdg.ac.uk/PETS2007/data.html.
3. O. Boiman and M. Irani. Detecting irregularities in images and in video. IJCV, 74(1):17-31, 2007.
4. N. Dalal and B. Triggs. Histogram of oriented gradients for human detection. In Proc. CVRP, pages 886-893, 2005.
5. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In Proc. IEEE Int'l Workshop on PETS, pages 65-72, 2005.
6. G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proc. ACM SIGKDD, pages 43-52, 2004.
7. A. Efros, A. Berg, G. Mori, and J. Malik. Recognizing action at a distance. In Proc. ICCV, pages 726-733, 2003.
8. L. Fei-Fei, A. Iyer, C. Kock, and P. Perona. What do we see in a glance of a scene? J. Vis., 7(10):1-29, 2007.
9. B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981.
10. T. Hospedales, S. Gong, and T. Xiang. A markov clustering topic model for mining behaviour in videos. In Proc. IEEE Int'l Conf. Computer Vision, pages 1165-1172, 2009.
11. C. John. A computational approach to edge detection. IEEE Trans. PAMI, 8(6):679-698, 1986.
12. Y. Ke, R. Sukthankar, and M. Hebert. Volumetric features for video event detection. International Journal of Computer Vision, 88(3):339-362, 2010.
13. I. Laptev, M. Marszalek, C. Schmid, and B. Rozeneld. Learning realistic human actions from movies. In Proc. CVPR, 2008.
14. J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos "in the wild". In Proc. CVPR, 2009.
15. K. Schindler and L. Gool. Action snippets: How many frames does human action recognition require? In Proc. CVPR, 2008.
16. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: a local SVM approach. In Proc. Int'l Conf. Pattern Recognition, pages 32-36, 2004.
17. P. Viola and M. Jones. Robust real-time face detection. IJCV, 57(2):137-154, 2004.
18. X. Wang, X. Ma, and E. Grimson. Unsupervised activity perception by hierarchical bayesian models. In Proc. IEEE Conf. CVPR, pages 1-8, 2007.
19. Y. Wang and G. Mori. Learning a discriminative hidden part model for human action recognition. In NIPS, 2008.