Modeling 4D human-object interactions for event and object recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 3272-3279

  Wei, Ping ^a,b   Zhao, Yibiao ^b   Zheng, Nanning ^a   Zhu, Song Chun ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OBJECT RECOGNITION; THREE DIMENSIONAL;

BEAM SEARCH ALGORITHMS; GEOMETRIC CONSTRAINT; HIERARCHICAL GRAPHS; HUMAN-OBJECT INTERACTION; KINECT CAMERAS; TEMPORAL DIMENSIONS; TEMPORAL STRUCTURES; VIDEO SEQUENCES;

VIDEO RECORDING;

EID: 84898777476     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.406     Document Type: Conference Paper

References (25)

1. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. 2005.
2. J. Gall, A. Fossati, and L. van Gool. Functional categorization of objects using real-time markerless motion capture. In CVPR, 2011.
3. J. J. Gibson. The Theory of Affordances. Lawrence Erlbaum, 1977.
4. H. Grabner, J. Gall, and L. J. V. Gool. What makes a chair a chair? In CVPR, 2011.
5. A. Gupta, A. Kembhavi, and L. Davis. Observing humanobject interactions: Using spatial and functional compatibility for recognition. IEEE TPAMI, 31(10), 2009.
6. X. R. Kevin Lai, Liefeng Bo and D. Fox. Detection-based object labeling in 3d scenes. In ICRA, 2012.
7. H. S. Koppula, R. Gupta, and A. Saxena. Learning human activities and object affordances from rgb-d videos. The International Journal of Robotics Research, 32(8), 2013.
8. F. Lv and R. Nevatia. Recognition and segmentation of 3-d human action using hmm and multi-class adaboost. In ECCV, 2006.
9. M. Marszałek, I. Laptev, and C. Schmid. Actions in context. In CVPR, 2009.
10. M. M̈uller and T. R̈oder. Motion templates for automatic classification and retrieval of motion capture data. In ACM SIGGRAPH/ Eurographics symposium on Computer animation, 2006.
11. B. Packer, K. Saenko, and D. Koller. A combined pose, object, and feature model for action understanding. In CVPR, 2012.
12. M. Pei, Y. Jia, and S.-C. Zhu. Parsing video events with goal inference and intent prediction. In ICCV, 2011.
13. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in large margin classifiers, 1999.
14. A. Prest, V. Ferrari, and C. Schmid. Explicit modeling of human-object interactions in realistic videos. Technical report, INRIA, 2011.
15. A. Prest, C. Schmid, and V. Ferrari.Weakly supervised learning of interactions between humans and objects. TPAMI, 34(3), 2012.
16. L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 1989.
17. S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In CVPR, 2012.
18. J. Shotton, A. W. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from single depth images. In CVPR, 2011.
19. J. Sung, C. Ponce, B. Selman, and A. Saxena. Unstructured human activity detection from rgbd images. In ICRA, 2012.
20. K. Tang, L. Fei-Fei, and D. Koller. Learning latent temporal structure for complex event detection. In CVPR, 2012.
21. C. Tillmann and H. Ney. Word reordering and a dynamic programming beam search algorithm for statistical machine translation. Computational Linguistics, 29, 2003.
22. J. Wang, Z. Liu, Y. Wu, and J. Yuan. Mining actionlet ensemble for action recognition with depth cameras. In CVPR, 2012.
23. J. X. Wu, A. Osuntogun, T. Choudhury, M. Philipose, and J. M. Rehg. A scalable approach to activity recognition based on object use. In ICCV, 2007.
24. B. Yao and L. Fei-Fei. Recognizing human-object interactions in still images by modeling the mutual context of objects and human poses. TPAMI, 34(9), 2012.
25. Y. Zhao and S.-C. Zhu. Scene parsing by integrating function, geometry and appearance models. In CVPR, 2013.