Sum-product networks for modeling activities with stochastic structure

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

Volumn , Issue , 2012, Pages 1314-1321

  Amer, Mohamed R ^a   Todorovic, Sinisa ^a  

^a OREGON STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY DETECTION; ALTERNATIVE CONFIGURATIONS; CLASSIFICATION ACCURACY; DATA SETS; EM ALGORITHMS; HUMAN ACTIVITIES; LINEAR COMPLEXITY; MIXTURE COMPONENTS; MOST PROBABLE EXPLANATION; NUMBER OF LAYERS; PRECISION AND RECALL; PRIMITIVE ACTIONS; SPACETIME; STOCHASTIC STRUCTURE; SUM PRODUCT; TERMINAL NODES; VARIABLE NUMBER; VISUAL WORD;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); SPORTS; STOCHASTIC SYSTEMS;

COMPUTER VISION;

EID: 84866674206     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247816     Document Type: Conference Paper

References (27)

1. J. Aggarwal and M. Ryoo. Human activity analysis: A review. ACM Comput. Surv., 43:16:1-16:43, 2011.
2. M. Albanese, R. Chellappa, N. Cuntoor, V. Moscato, A. Picariello, V. S. Subrahmanian, and O. Udrea. PADS: A probabilistic activity detection framework for video data. IEEE TPAMI, 32:2246-2261, 2010.
3. M. Amer and S. Todorovic. A Chains model for localizing group activities in videos. In ICCV, 2011.
4. S. Bhattacharya, R. Sukthankar, R. Jin, and M. Shah. A probabilistic representation for efficient large scale visual recognition tasks. In CVPR, 2011.
5. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In IWVSPETS, 2005.
6. A. Gupta, P. Srinivasan, J. Shi, and L. Davis. Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In CVPR, 2009.
7. R. Hamid, S. Maddi, A. Bobick, and I. Essa. Structure fromstatistics: Unsupervised activity analysis using suffix trees. In ICCV, pages 1-8, 2007.
8. N. Jojic and A. Perina. Multidimensional counting grids: Inferring word order from disordered bags of words. In UAI, 2011.
9. A. Kovashka and K. Grauman. Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In CVPR, 2010.
10. T. Lan, Y. Wang, W. Yang, and G. Mori. Beyond actions: Discriminative models for contextual group activities. In NIPS, 2010.
11. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR,2008.
12. Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR,2011.
13. Z. Lin, Z. Jiang, and L. S. Davis. Recognizing actions by shape-motion prototype trees. In ICCV, 2009.
14. J. Niebles, C.-W. Chen, and L. Fei-Fei. Modeling temporal structure of decomposable motion segments for activityclassification. In ECCV, 2010.
15. J. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. IJCV, 79(3):299-318, 2008.
16. S. Oh and et al. A large-scale benchmark dataset for event recognition in surveillance video. In CVPR, 2011.
17. A. Perina and N. Jojic. Image analysis by counting on a grid. In CVPR, 2011.
18. H. Poon and P. Domingos. Sum-product networks: A new deep architecture. In UAI, 2011.
19. M. S. Ryoo and J. K. Aggarwal. Spatiotemporal relationship match: Video structure comparison for recognition of complex human activities. In ICCV, 2009.
20. C. Schueldt, I. Laptev, and B. Caputo. Recognizing human actions: A local SVM approach. In ICPR, 2004.
21. Z. Si, M. Pei, B. Yao, and S.-C. Zhu. Unsupervised learning of event AND-OR grammar and semantics from video. In ICCV, 2011.
22. S. D. Tran and L. S. Davis. Event modeling and recognition using Markov logic networks. In ECCV, 2008.
23. P. K. Turaga, R. Chellappa, V. S. Subrahmanian, and O. Udrea. Machine recognition of human activities: A survey. IEEE Trans. Circuits Syst. Video Techn., 18(11):1473-1488, 2008.
24. M. M. Ullah, S. N. Parizi, and I. Laptev. Improving bag-of features action recognition with non-local cues. In BMVC, 2010.
25. D. Weinland, R. Ronfard, and E. Boyer. A survey of vision-based methods for action representation, segmentation and recognition. In CVIU, volume 115, pages 224-241, 2011.
26. X.Wu, D. Xu, L. Duan, and J. Luo. Action recognition using context and appearance distribution features. In CVPR, 2011.
27. Z. Zeng and Q. Ji. Knowledge based activity recognition with Dynamic Bayesian Network. In ECCV, 2010.