Pose Adaptive Motion Feature Pooling for Human Action Analysis

International Journal of Computer Vision

Volumn 111, Issue 2, 2015, Pages 229-248

  Ni, Bingbing ^a   Moulin, Pierre ^b   Yan, Shuicheng ^c  

^a ADVANCED DIGITAL SCIENCES CENTER   (Singapore)

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

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Action recognition; Adaptive feature pooling; Human pose

Indexed keywords

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

ACTION RECOGNITION; ACTION REPRESENTATIONS; ADAPTIVE FEATURES; DETECTION PERFORMANCE; FUNDAMENTAL LIMITATIONS; HUMAN ACTION ANALYSIS; HUMAN POSE; HUMAN-ACTION RECOGNITION;

ASSISTED LIVING;

EID: 84921906046     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-014-0742-4     Document Type: Article

References (42)

1. Andrews, S., & Tsochantaridis, I. (2003). Support vector machines for multiple instance learning. In: Advances in neural information processing systems (pp. 561–568). MIT Press.
2. Bourdev, L., & Malik, J. (2009). Poselets: Body part detectors trained using 3d human pose annotations. In: International conference on computer vision, URL http://www.eecs.berkeley.edu/~lbourdev/poselets
3. Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent System and Technology, 2(27), 1–27.
4. Chen, Q., Song, Z., Hua, Y., Huang, Z., & Yan, S. (2011). Hierarchical matching with side information for image classification. In: International conference on computer vision and pattern recognition.
5. Choi, J., Jeon, W.J., & Lee, S.C. (2008). Spatio-temporal pyramid matching for sports videos. In: ACM multimedia information retrieval.
6. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In: International conference on computer vision and pattern recognition (pp. 886–893).
7. Dollar, P., Rabaud, V., Cottrell, G., & Belongie, S. (2005). Behavior recognition via sparse spatio-temporal features. In: VS-PETS.
8. Duchenne, O., Laptev, I., Sivic, J., Bach, F., & Ponce, J. (2009). Automatic annotation of human actions in video. In: International conference on computer vision (pp. 1491–1498).
9. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627–1645.
10. Girshick, R.B., Felzenszwalb, P.F., & McAllester, D. (2012). Discriminatively trained deformable part models, release 5. http://people.cs.uchicago.edu/~rbg/latent-release5/
11. Yang, J., YG., Yu, K., Huang, T. (2009). Linear spatial pyramid matching using sparse coding for image classification. In: International conference on computer vision and pattern recognition.
12. Jiang, Y., Yuan, J., Yu, G. (2012). Randomized spatial partition for scene recognition. In: European conference on computer vision.
13. Kanan, C., Cottrell, G. (2010). Robust classification of objects, faces, and flowers using natural image statistics. In: International conference on computer vision and pattern recognition.
14. Klaser, A., Marszalek, M., & Schmid, C. (2008). A spatio-temporal descriptor based on 3d gradients. In: British machine vision conference.
15. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011). HMDB: A large video database for human motion recognition. In: International conference on computer vision.
16. Laptev, I., Lindeberg, T. (2003). Space-time interest points. In: International conference on computer vision.
17. Lazebnik, S., Schmid, C., Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: International conference on computer vision and pattern recognition.
18. Lv, F., & Nevatia, R. (2007). Single view human action recognition using key pose matching and viterbi path searching. In: International conference computer vision and pattern recognition.
19. Marszalek, M., Laptev, I., & Schmid, C. (2009). Actions in context. In: International conference on computer vision and pattern recognition (pp. 2929–2936). Retrieved June, 2009.
20. Ni, B., Wang, G., & Moulin, P. (2011). RGBD-HuDaAct: A color-depth video database for human daily activity recognition. In: ICCV workshops (pp. 1147–1153).
21. Niebles, J.C., Chen, C.W., & Fei-fei, L. (2010). Modeling temporal structure of decomposable motion segments for activity classification. In: European conference on computer vision (pp. 392–405).
22. Perronnin, F., Sánchez, J., & Mensink, T. (2010). Improving the fisher kernel for large-scale image classification. In: European conference on computer vision (pp. 143–156).
23. Raptis, M., & Sigal, L. (2013). Poselet key-framing: A model for human activity recognition. In: International conference on computer vision and pattern recognition (pp. 2650–2657).
24. Raptis, M., Kokkinos, I., & Soatto, S. (2012). Discovering discriminative action parts from mid-level video representations. In: International conference on computer vision and pattern recognition.
25. Russakovsky, O., Lin, Y., Yu, K., & Fei-Fei, L. (2012). Object-centric spatial pooling for image classification. In: European conference on computer vision.
26. Ryoo, M.S., & Aggarwal, J. (2009). Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In: International conference on computer vision (pp. 1593–1600).
27. Satkin, S., Hebert, M. (2010). Modeling the temporal extent of actions. In: European conference on computer vision (pp. 536–548).
28. Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local SVM approach. In: International conference on pattern recognition.
29. Shi, Q., Wang, L., Cheng, L., & Smola, A. (2011). Discriminative human action segmentation and recognition using semi-markov models. International Journal of Computer Vision, 93(1), 22–32.
30. Shimada, A., Kondo, K., Deguchi, D., Morin, G., & Stern, H. (2013). Kitchen scene context based gesture recognition: A contest in icpr2012. In: Advances in depth image analysis and applications. (vol. 7854), (pp. 168–185), URL http://www.murase.m.is.nagoya-u.ac.jp/KSCGR/index.html
31. Tang, K., Fei-fei, L., & Koller, D. (2012). Learning latent temporal structure for complex event detection. In: International conference on computer vision and pattern recognition.
32. Vahdat, A., Gao, B., Ranjbar, M., & Mori, G. (2011). A discriminative key pose sequence model for recognizing human interactions. In: ICCV workshop (pp. 1729–1736).
33. Wang, G., & Forsyth, D. (2009). Joint learning of visual attributes, object classes and visual saliency. In: International conference on computer vision.
34. Wang, H., & Schmid, C. (2013). Action recognition with improved trajectories. In: International conference on computer vision.
35. Wang, H., Kläser, A., Schmid, C., & Cheng-Lin, L. (2011). Action recognition by dense trajectories. In: International conference on computer vision and pattern recognition (pp. 3169–3176).
36. Wang, H., Kläser, A., Schmid, C., & Liu, C. L. (2013). Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision, 103(1), 60–79.
37. Wang, J., Liu, Z., Wu, Y., & Yuan, J. (2012). Mining actionlet ensemble for action recognition with depth cameras. In: International conference on computer vision and pattern recognition (pp. 1290–1297).
38. Wang, Y., & Mori, G. (2011). Hidden part models for human action recognition: Probabilistic versus max margin. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(7), 1310–1323.
39. Wolf, C., Mille, J., Lombardi, L., Celiktutan, O., Jiu, M., Baccouche, M., Dellandrea, E., Bichot, C., Garcia, C., & Sankur, B. (2012). The liris human activities dataset and the icpr 2012 human activities recognition and localization competition. Technical report RR-LIRIS-2012-004, LIRIS laboratory, URL http://liris.cnrs.fr/harl2012/evaluation.html
40. Yakhnenko, O., & Verbeek, J. (2011). Region-based image classification with a latent SVM model. Technical report, INRIA.
41. Yamato, J., Ohya, J., & Ishii, K. (1992). Recognizing human action in time-sequential images using hidden markov model. In: International conference on computer vision and pattern recognition (pp. 379–385).
42. Yuan, J., Liu, Z., & Wu, Y. (2011). Discriminative video pattern search for efficient action detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9), 1728–1743.