STFC: Spatio-temporal feature chain for skeleton-based human action recognition

Journal of Visual Communication and Image Representation

Volumn 26, Issue , 2015, Pages 329-337

  Ding, Wenwen ^a   Liu, Kai ^a   Cheng, Fei ^a   Zhang, Jin ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

3D action feature representation; 3D trajectory segmentation; B spline fitting; Computer vision; RGB D dataset; Skeleton joints; SVM learning; View invariant representation

Indexed keywords

CHAINS; COMPUTER VISION; GESTURE RECOGNITION; IMAGE RECOGNITION; MUSCULOSKELETAL SYSTEM;

3-D TRAJECTORY; B-SPLINE FITTINGS; FEATURE REPRESENTATION; RGB-D DATASET; SKELETON JOINTS; SVM LEARNING; VIEW INVARIANTS;

MOTION ESTIMATION;

EID: 84920198976     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2014.10.009     Document Type: Article

References (22)

1. R. Klette, G. Tee, Understanding Human Motion: A Historic Review, Springer, 2008.
2. N. Villaroman, D. Rowe, B. Swan, Teaching natural user interaction using OpenNI and the Microsoft Kinect sensor, in: Proceedings of the 2011 Conference on Information Technology Education, ACM, 2011, pp. 227-232.
3. G. Mastorakis, D. Makris, Fall detection system using Kinects infrared sensor, J. Real-Time Image Process. (2012) 1-12.
4. L. Chen, H. Wei, J. Ferryman, A survey of human motion analysis using depth imagery, Pattern Recogn. Lett. 34 (15) (2013) 1995-2006.
5. M. Ye, Q. Zhang, L. Wang, J. Zhu, R. Yang, J. Gall, A survey on human motion analysis from depth data, in: Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, Springer, 2013, pp. 149-187.
6. J. Wang, Z. Liu, Y. Wu, Learning actionlet ensemble for 3D human action recognition, in: Human Action Recognition with Depth Cameras, Springer, 2014, pp. 11-40.
7. K. Li, J. Hu, Y. Fu, Modeling complex temporal composition of actionlets for activity prediction, in: Computer Vision-ECCV 2012, Springer, 2012, pp. 286-299.
8. B. Chakraborty, M.B. Holte, T.B. Moeslund, J. Gonzàlez, Selective spatio-temporal interest points, Comput. Vis. Image Understan. 116 (3) (2012) 396-410.
9. Z. Zhang, Microsoft kinect sensor and its effect, MultiMedia, IEEE 19 (2) (2012) 4-10.
10. G. Johansson, Visual Motion Perception, Scientific American.
11. C. Rao, A. Yilmaz, M. Shah, View-invariant representation and recognition of actions, Int. J. Comput. Vis. 50 (2) (2002) 203-226.
12. L.W. Campbell, A.F. Bobick, Recognition of human body motion using phase space constraints, in: Proceedings., Fifth International Conference on Computer Vision, 1995, IEEE, 1995, pp. 624-630.
13. W. Li, Z. Zhang, Z. Liu, Action recognition based on a bag of 3D points, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2010, pp. 9-14.
14. L. Xia, C.-C. Chen, J. Aggarwal, View invariant human action recognition using histograms of 3D joints, in: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2012, pp. 20-27.
15. J. Sung, C. Ponce, B. Selman, A. Saxena, Unstructured human activity detection from rgbd images, in: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2012, pp. 842-849.
16. C. Ellis, S.Z. Masood, M.F. Tappen, J.J. Laviola Jr., R. Sukthankar, Exploring the trade-off between accuracy and observational latency in action recognition, Int. J. Comput. Vis. 101 (3) (2013) 420-436.
17. X. Yang, Y. Tian, Effective 3D action recognition using eigenjoints, J. Visual Commun. Image Represent. 25 (1) (2014) 2-11.
18. H. Gjøystdal, B. Ursin, Inversion of reflection times in three dimensions, Geophysics 46 (7) (1981) 972-983.
19. P. Senin, Dynamic Time Warping Algorithm Review, Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA, 2008, pp. 1-23.
20. J. Martens, I. Sutskever, Learning recurrent neural networks with hessian-free optimization, in: Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011, 1033-1040.
21. M. Müller, T. Röder, Motion templates for automatic classification and retrieval of motion capture data, in: Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation, Eurographics Association, 2006, pp. 137-146.
22. F. Lv, R. Nevatia, Recognition and segmentation of 3-d human action using hmm and multi-class adaboost, in: Computer Vision-ECCV 2006, Springer, 2006, pp. 359-372.