Human action recognition by mining discriminative segment with novel skeleton joint feature

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8294 LNCS, Issue , 2013, Pages 517-527

  Zou, Weijia ^a   Wang, Baoyuan ^b   Zhang, Rui ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Action recognition; Dynamic pooling; Multiple instance learning; Skeleton feature

Indexed keywords

DATA PROCESSING; GESTURE RECOGNITION; LEARNING SYSTEMS; MOTION ESTIMATION;

3D SKELETON; ACTION RECOGNITION; BENCHMARK DATASETS; HUMAN-ACTION RECOGNITION; MULTIPLE INSTANCE LEARNING; SKELETON FEATURE; SKELETON JOINTS; STATE-OF-THE-ART PERFORMANCE;

MUSCULOSKELETAL SYSTEM;

EID: 84894188828     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-03731-8_48     Document Type: Conference Paper

References (16)

1. Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1290-1297. IEEE (2012).
2. Yang, X., Tian, Y.: Effective 3D action recognition using eigenjoints. Journal of Visual Communication and Image Representation (2013).
3. Shao, L., Ji, L.: Motion histogram analysis based key frame extraction for human action/activity representation. In: Canadian Conference on Computer and Robot Vision, CRV 2009, pp. 88-92. IEEE (2009).
4. Ellis, C., Masood, S.Z., Tappen, M.F., LaViola Jr., J.J., Sukthankar, R.: Exploring the trade-off between accuracy and observational latency in action recognition. International Journal of Computer Vision 101(3), 420-436 (2013).
5. Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976-990 (2010).
6. Nguyen, M.H., Torresani, L., De la Torre, F., Rother, C.: Weakly supervised discriminative localization and classification: a joint learning process. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1925-1932. IEEE (2009).
7. Schindler, K., Van Gool, L.: Action snippets: How many frames does human action recognition require? In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1-8. IEEE (2008).
8. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW, pp. 9-14. IEEE (2010).
9. Ohn-Bar, E., Trivedi, M.M.: Joint angles similiarities and hog2 for action recognition (2013).
10. Xia, L., Chen, C.C., Aggarwal, J.: View invariant human action recognition using histograms of 3D joints. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20-27. IEEE (2012).
11. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. Advances in Neural Information Processing Systems 15, 561-568 (2002).
12. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627-1645 (2010).
13. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871-1874 (2008).
14. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794-1801. IEEE (2009).
15. Liu, L., Wang, L., Liu, X.: In defense of soft-assignment coding. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2486-2493. IEEE (2011).
16. Zhu, J., Zou, W., Yang, X., Zhang, R., Zhou, Q., Zhang, W.: Image classification by hierarchical spatial pooling with partial least squares analysis. In: Proceedings of the British Machine Vision Conference, pp. 102.1-102.11. BMVA Press (2012).