Incremental activity modeling and recognition in streaming videos

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

Volumn , Issue , 2014, Pages 796-803

  Hasan, Mahmudul ^a,b   Roy Chowdhury, Amit K ^a,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Dept of Computer Science ^*  (United States)

^c UCR   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; VIDEO STREAMING;

ACTIVE LEARNING SYSTEMS; ACTIVITY LEARNING; ACTIVITY MODELING; ACTIVITY RECOGNITION; HUMAN ACTIVITIES; HUMAN ACTIVITY RECOGNITION; STATE OF THE ART; STATE-OF-THE-ART APPROACH;

LEARNING SYSTEMS;

EID: 84911421120     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.107     Document Type: Conference Paper

References (25)

1. C. Schuldt, I. Laptev, and B. Caputo, "Recognizing human actions: a local svm approach, " in ICPR, 2004.
2. J. Liu, J. Luo, and M. Shah, "Recognizing realistic actions from videos "in the wild", " in CVPR, 2009.
3. S. Oh, A. Hoogs, and et al., "A large-scale benchmark dataset for event recognition in surveillance video, " in CVPR, 2011.
4. R. Poppe, "A survey on vision-based human action recognition, " Image and Vision Computing, vol. 28, no. 6, 2010.
5. I. Laptev, "On space-time interest points, " IJCV, 2005.
6. S. Sadanand and J. Corso, "Action bank: A high-level representation of activity in video, " in CVPR, 2012.
7. B. Solmaz, S. M. Assari, and M. Shah, "Classifying web videos using a global video descriptor, " MVAP, 2012.
8. R. Minhas, A. Mohammed, and Q. Wu, "Incremental learning in human action recognition based on snippets, " IEEE TCSVT, 2012.
9. W. Brendel and S. Todorovic, "Learning spatiotemporal graphs of human activities, " in ICCV, 2011.
10. Z. Si, M. Pei, B. Yao, and S.-C. Zhu, "Unsupervised learning of event and-or grammar and semantics from video, " in ICCV, 2011.
11. Y. Zhu, N. M. Nayak, and A. K. Roy-Chowdhury, "Context-aware modeling and recognition of activities in video, " in CVPR, 2013.
12. R. Polikar, L. Upda, S. Upda, and V. Honavar, "Learn++: an incremental learning algorithm for supervised neural networks, " IEEE TSMC Part:C, vol. 31, no. 4, 2001.
13. H. He, S. Chen, K. Li, and X. Xu, "Incremental learning from stream data, " IEEE TNN, vol. 22, no. 12, pp. 1901-1914, 2011.
14. B. Settles, "Active learning, " Morgan & Claypool, 2012.
15. J. M. Buhmann, A. Vezhnevets, and V. Ferrari, "Active learning for semantic segmentation with expected change, " in CVPR, 2012.
16. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell, "Active learning with gaussian processes for object categorization, " in ICCV, 2007.
17. C. Loy, T. Xiang, and S. Gong, "Stream-based active unusual event detection, " in ACCV, 2011.
18. X. Liu and J. Zhang, "Active learning for human action recognition with gaussian processes, " in ICIP, 2011.
19. K. Reddy, J. Liu, and M. Shah, "Incremental action recognition using feature-tree, " in ICCV, 2009.
20. Z. Zivkovic, "Improved adaptive gaussian mixture model for background subtraction, " in ICPR, 2004.
21. P. F. Felzenszwalb, R. B. Girshic, and D. McAllester. Discrimina-tively trained deformable part models, release 4. [Online]. Available: http://people.cs.uchicago.edu/pff/latent-release4/
22. B. Song, T. Jeng, E. Staudt, and A. Roy-Chowdury, "A stochastic graph evolution framework for robust multi-target tracking, " in ECCV, 2010.
23. R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal., "Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions, " in CVPR, 2009.
24. R. Schapire, "Strength of weak learning, " ML, 1990.
25. Y. Hao, Y. Chen, J. Zakaria, B. Hu, T. Rakthanmanon, and E. Keogh, "Towards never-ending learning from time series streams, " in SIGKDD, 2013.