Localizing volumetric motion for action recognition in realistic videos

MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Volumn , Issue , 2009, Pages 505-508

  Wu, Xiao ^a,c   Ngo, Chong Wah ^b   Li, Jintao ^a   Zhang, Yongdong ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Human action recognition; Keypoint trajectory; Mean shift clustering; Motion subspace learning; Realistic videos

Indexed keywords

3D GRADIENTS; ACTION RECOGNITION; ARBITRARY POSITIONS; COMPREHENSIVE MODELING; CONTEXTUAL INFORMATION; DATA SETS; DESCRIPTORS; HUMAN ACTION RECOGNITION; INTEREST POINTS; KEYPOINTS; LONG TERM DYNAMICS; MEAN SHIFT; MOTION DYNAMICS; MOTION VARIATION; POINT TRAJECTORY; SPACE TIME; SPATIO-TEMPORAL; SUBSPACE LEARNING; TIME DIMENSION; VOLUME OF INTEREST;

EVOLUTIONARY ALGORITHMS; GESTURE RECOGNITION; METHOD OF MOMENTS; MULTIMEDIA SYSTEMS; TECHNICAL PRESENTATIONS; THREE DIMENSIONAL;

TRAJECTORIES;

EID: 72449142170     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1631272.1631342     Document Type: Conference Paper

References (12)

1. J. Sun, X. Wu, SC. Yan, LF. Cheong, TS. Chua and J. Li. Hierarchical spatio-temporal context modeling for action recognition. CVPR, 2009.
2. F. Wang, Y. Jiang, and C. Ngo. Video event detection using motion relativity and visual relatedness. ACM Multimedia, 2008.
3. J. Liu, J. Luo, et al. Recognizing realistic actions from videos 'in the Wild'. CVPR, 2009.
4. B. Morris, et al. A survey of vision-based trajectory learning and analysis for surveillance. TCSVT, 2008.
5. I. Laptev, M. Marszałek, C. Schmid, et al. Learning realistic human actions from movies. CVPR, 2008.
6. D. Batra, T. Chen and R. Sukthankar. Space-Time shapelets for action recognition. IEEE WMVC, 2008.
7. L. Gorelick, M. Blank, E. Shechtman, et al. Actions as space-time shapes. TPAMI, 2007.
8. R. Tron, et al. A benchmark for the comparison of 3D motion segmentation algorithms. CVPR, 2008.
9. Y. Cheng, et al. Mean shift, mode seeking, and clustering. TPAMI, 1995.
10. P. Dollar, V. Rabaud, et al. Behavior recognition via sparse spatio-temporal features. In VS-PETS, 2005.
11. X. Wu, Y. Zhang, Y. Wu, J. Guo and J. Li. Invariant visual patterns for video copy detection. ICPR, 2008.
12. OpenCV: sourceforge.net/projects/opencvlibrary.