Action recognition using discriminative structured trajectory groups

Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015

Volumn , Issue , 2015, Pages 899-906

  Atmosukarto, Indriyati ^a,b   Ahuja, Narendra ^c   Ghanem, Bernard ^d  

^a SINGAPORE INSTITUTE OF TECHNOLOGY   (Singapore)

^b Advanced Digital Sciences Center ADSC   (Singapore)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^d KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); SUPPORT VECTOR MACHINES; TRAJECTORIES;

ACTION RECOGNITION; ASSOCIATED FEATURE; BENCHMARK DATASETS; CLASSIFICATION PERFORMANCE; COMPLEX COMPUTATION; MULTIPLE-INSTANCE LEARNING; SPATIO-TEMPORAL RELATIONSHIPS; STATE-OF-THE-ART APPROACH;

LEARNING SYSTEMS;

EID: 84925400524     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2015.124     Document Type: Conference Paper

References (27)

1. C. S. A. Gaidon, Z. Harchaoui. Recognizing activities with cluster-trees of tracklets. In BMVC, 2012.
2. S. Ali and M. Shah. Human action recognition in videos using kinematic features and MIL. PAMI, 32:288-303, 2010.
3. S. Andrews, I. Tsochantaridis, and T. Hoffman. Support vector machines for MIL. In NIPS, 2002.
4. I. Atmosukarto, B. Ghanem, and N. Ahuja. Trajectory-based fisher kernel representation for action recognition in videos. In ICPR, 2012.
5. T. Brox and J. Malik. Object segmentation by long term analysis of point trajectories. In ECCV, 2010.
6. R. Cuingnet, J. A. Glaunes, M. Chupin, H. Benali, O. Colliot, and T. A. D. N. Initiative. Spatial and anatomical regularization of svm: A general framework for neuroimaging data. PAMI, 35(3), 2013.
7. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. PAMI, 32(9), 2010.
8. G. Herman, G. Ye, Y. Wang, J. Xu, and B. Zhang. Multiinstance learning with relational information of instances. In WACV, 2009.
9. A. Jain, A. Gupta, M. Rodriguez, and L. Davis. Representing videos using mid-level discriminative patches. In CVPR, 2010.
10. T. Lan, Y. Wang, and G. Mori. Discriminative figure-centric models for joint action localization and recognition. In ICCV, 2011.
11. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008.
12. A. Patron-Perez, M. Marszalek, I. Reid, and A. Zisserman. Structured learning of human interactions in tv shows. PAMI, 2012.
13. A. Patron-Perez, M. Marszalek, A. Zisserman, and I. Reid. High five: Recognising human interactions in tv shows. In BMVC, 2010.
14. M. Raptis, I. Kokkinos, and S. Soatto. Discovering discriminative action parts from mid-level video representations. In CVPR, 2012.
15. M. D. Rodriguez, J. Ahmed, and M. Shah. Actionmach = a spatio-temporal maximum average correlation height filter for action recognition. In CVPR, 2008.
16. M. Sapienza, F. Cuzzolin, and P. H.S.Torr. Learning discriminative space-time actions from weakly labelled videos. In BMVC, 2012.
17. S. Tabatabaei, M. Coates, and M. Rabbat. Ganc: Greedy agglomerative normalized cut. arXiv:1105.0974, 2011.
18. Y. Tian, R. Sukthankar, and M. Shah. Spatiotemporal deformable part models for action detection. In CVPR, 2013.
19. H.Wang, A. Klaser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In CVPR, 2011.
20. H. Wang, M. Ullah, A. Klaser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In BMVC, 2009.
21. L. Wang, Y. Qiao, and X. Tang. Motionlets: Mid-level 3d parts for human motion recognition. In CVPR, 2013.
22. J. Warrell and P. H. Torr. Multiple-instance learning with structured bag models. In EMMCVPR, 2011.
23. S.Wu, O. Oreifej, and M. Shah. Action recognition in videos acquired by a moving camera using motion decomposition of lagrangian particle trajectories. In ICCV, 2011.
24. J. Yang. Mill: A multiple instance learning library.
25. Y. Yi and Y. Lin. Human action recognition with salient trajectories. Signal Processing, 93:2932-2941, 2013.
26. Z.-Q. Zeng and J. Gao. Improving svm classification with imbalance data set. In NIPS, 2009.
27. Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. MIL by treating instances as non-iid samples. In ICML, 2009.