Watch-n-patch: Unsupervised understanding of actions and relations

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

Volumn 07-12-June-2015, Issue , 2015, Pages 4362-4370

  Wu, Chenxia ^a,b   Zhang, Jiemi ^a   Savarese, Silvio ^b   Saxena, Ashutosh ^a,b  

^a Department of Computer Science and School of Operations Research and Information Engineering   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

ACTION SEGMENTATION; COMPLEX ACTIVITY; DAILY ACTIVITY; HUMAN ACTIVITIES; LONG-RANGE ACTION; NOVEL APPLICATIONS; PROBABILISTIC MODELING; TEMPORAL RELATION;

PATTERN RECOGNITION;

EID: 84959198612     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299065     Document Type: Conference Paper

References (43)

1. Kinect v2 sensor. http://www. microsoft. com/en-us/kinectforwindows/develop/
2. S. M. Assari, A. R. Zamir, and M. Shah. Video classification using semantic concept co-occurrences. In CVPR, 2014
3. S. Bhattacharya, M. M. Kalayeh, R. Sukthankar, and M. Shah. Recognition of complex events: Exploiting temporal dynamics between underlying concepts. In CVPR, 2014
4. D. M. Blei and J. D. Lafferty. A correlated topic model of science. The Annals of Applied Statistics, 1(1):17-35, 2007
5. D. M. Blei and J. D. Lafferty. Topic models. Text mining: classification, clustering, and applications, 10:71, 2009
6. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993-1022, 2003
7. P. Bojanowski, R. Lajugie, F. Bach, I. Laptev, J. Ponce, C. Schmid, and J. Sivic. Weakly supervised action labeling in videos under ordering constraints. In ECCV, 2014
8. P. Dollár and C. L. Zitnick. Structured forests for fast edge detection. In ICCV, 2013
9. O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce. Automatic annotation of human actions in video. In ECCV, 2009
10. T. A. Faruquie, P. K. Kalra, and S. Banerjee. Time based activity inference using latent dirichlet allocation. In BMVC, 2009
11. A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. Bayesian data analysis. CRC press, 2013
12. M. Hoai, Z. zhong Lan, and F. De la Torre. Joint segmentation and classification of human actions in video. In CVPR, 2011
13. S. Jones and L. Shao. Unsupervised spectral dual assignment clustering of human actions in context. In CVPR, 2014
14. V. Kantorov and I. Laptev. Efficient feature extraction, encoding and classification for action recognition. In CVPR, 2014
15. Y. Ke, R. Sukthankar, and M. Hebert. Event detection in crowded videos. In ECCV, 2007
16. D. I. Kim and E. B. Sudderth. The doubly correlated nonparametric topic model. In NIPS, 2011
17. A. Kläser, M. Marszaek, C. Schmid, and A. Zisserman. Human focused action localization in video. In International Workshop on Sign, Gesture, and Activity (SGA) in Conjunc-tion with ECCV, 2010
18. H. S. Koppula and A. Saxena. Anticipating human activities using object affordances for reactive robotic response. In RSS, 2013
19. H. S. Koppula and A. Saxena. Learning spatio-temporal structure from RGB-D videos for human activity detection and anticipation. In ICML, 2013
20. H. Kuehne, A. Arslan, and T. Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In CVPR, 2014
21. I. Laptev and P. Perez. Retrieving actions in movies. In ICCV, 2007
22. Y.-Y. Lin, J.-H. Hua, N. C. Tang, M.-H. Chen, and H.-Y. Mark Liao. Depth and skeleton associated action recognition without online accessible rgb-d cameras. In CVPR, 2014
23. J. Liu, B. Kuipers, and S. Savarese. Recognizing human actions by attributes. In CVPR, 2011
24. S. Mathe and C. Sminchisescu. Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition. TPAMI, 2014
25. B. Ni, V. R. Paramathayalan, and P. Moulin. Multiple granularity analysis for fine-grained action detection. In CVPR, 2014
26. J. C. Niebles, C.-W. Chen, and L. Fei-Fei. Modeling temporal structure of decomposable motion segments for activity classification. In ECCV, 2010
27. H. Pirsiavash and D. Ramanan. Parsing videos of actions with segmental grammars. In CVPR, 2014
28. N. Reljin, S. McDaniel, D. Pokrajac, N. Pejcic, T. Vance, A. Lazarevic, and L. J. Latecki. Small moving targets detection using outlier detection algorithms. Proc. SPIE, 7698:769804-769804-12, 2010
29. S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In CVPR, 2012
30. A. Saxena, A. Jain, O. Sener, A. Jami, D. K. Misra, and H. S. Koppula. Robo brain: Large-scale knowledge engine for robots. Tech Report arxiv, 2014
31. B. Schiele. A database for fine grained activity detection of cooking activities. In CVPR, 2012
32. Q. Shi, L. Cheng, L. Wang, and A. Smola. Human action segmentation and recognition using discriminative semi-markov models. IJCV, 93(1):22-32, 2011
33. J. Sung, C. Ponce, B. Selman, and A. Saxena. Unstructured human activity detection from rgbd images. In ICRA, 2012
34. K. Tang, L. Fei-Fei, and D. Koller. Learning latent temporal structure for complex event detection. In CVPR, 2012
35. Y. Tian, R. Sukthankar, and M. Shah. Spatiotemporal deformable part models for action detection. In CVPR, 2013
36. R. Vemulapalli, F. Arrate, and R. Chellappa. Human action recognition by representing 3d skeletons as points in a lie group. In CVPR, 2014
37. N. N. Vo and A. F. Bobick. From stochastic grammar to bayes network: Probabilistic parsing of complex activity. In CVPR, 2014
38. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu. Action Recognition by Dense Trajectories. In CVPR, 2011
39. X. Wang and Q. Ji. A hierarchical context model for event recognition in surveillance video. In CVPR, 2014
40. X. Wang and A. McCallum. Topics over time: A non-markov continuous-time model of topical trends. In KDD, 2006
41. C. Wu, I. Lenz, and A. Saxena. Hierarchical semantic labeling for task-relevant rgb-d perception. In RSS, 2014
42. D. Wu and L. Shao. Leveraging hierarchical parametric networks for skeletal joints based action segmentation and recognition. In CVPR, 2014
43. Y. Yang, I. Saleemi, and M. Shah. Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions. TPAMI, 35(7):1635-1648, 2013.