Skeleton based Human Action Recognition using Kinect

Print
IJCA Proceedings on Recent Trends in Future Prospective in Engineering and Management Technology
© 2016 by IJCA Journal
RTFEM 2016 - Number 1
Year of Publication: 2016
Authors:
Ayushi Gahlot
Purvi Agarwal
Akshya Agarwal
Vijai Singh
Amit Kumar Gautam

Ayushi Gahlot, Purvi Agarwal, Akshya Agarwal, Vijai Singh and Amit Kumar Gautam. Article: Skeleton based Human Action Recognition using Kinect. IJCA Proceedings on Recent Trends in Future Prospective in Engineering and Management Technology RTFEM 2016(1):9-13, July 2016. Full text available. BibTeX

@article{key:article,
	author = {Ayushi Gahlot and Purvi Agarwal and Akshya Agarwal and Vijai Singh and Amit Kumar Gautam},
	title = {Article: Skeleton based Human Action Recognition using Kinect},
	journal = {IJCA Proceedings on Recent Trends in Future Prospective in Engineering and Management Technology},
	year = {2016},
	volume = {RTFEM 2016},
	number = {1},
	pages = {9-13},
	month = {July},
	note = {Full text available}
}

Abstract

This paper covers the aspects of action recognition using Kinect technology by human skeletal tracking. Microsoft Kinect is one of the latest advancements in Computer Vision based HCI (Human Computer Interaction). The paper is focused on how the Kinect sensor captures the 3D information of a scene and recognizes the action being performed by the human body by retrieving the depth image information and real-time skeletal tracking. The Kinect technology has revolutionized the way humans interact with the machines. It has a wide range of applications areas. The paper also covers one of the proposed approach to skeletal based action recognition using Kinect.

References

  • Gu, J. , Ding, X. , Wang, S. , Wu, Y. : Action and gait recognition from recovered 3-d human joints. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Trans. on 40(4), 1021–1033 (2010)
  • J. Shotton et al. , ''Real-Time Human Pose Recognition in Parts from a Single Depth Image,'' Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), IEEE CS Press, 2011, pp. 12971304.
  • S. Izadi et al. , ''Kinect Fusion: Real-Time Dynamic 3D Surface Reconstruction and Interaction,'' Proc. ACM SIGGRAPH, 2011.
  • W. Li, Z. Zhang, and Z. Liu, ''Action Recognition Based on A Bag of 3D Points,''Proc. IEEE Int'l Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB), IEEE CS Press, 2010, pp. 914.
  • Z. Ren, J. Yuan, and Z. Zhang, ''Robust Hand Gesture Recognition Based on Finger-Earth Movers Distance with a Commodity Depth Camera,'' Proc. 19th ACM Int'l Conf. Multimedia (ACM MM), ACM Press, 2011, pp. 10931096.
  • Ballan, L. , Bertini, M. , Del Bimbo, A. , Seidenari, L. , Serra, G. : E?ective codebooks for human action representation and classi?cation in unconstrained videos. Multimedia, IEEE Transactions on 14(4), 1234–1245 (2012)
  • Isaac Cohen, Hongxia Li, "Inference of Human Postures by Classification of 3D Human Body Shape", International Workshop on Analysis and Modeling o f Faces and Gestures, pp. 74 – 81, 2003.
  • D. M. Gavrila and L. S. Davis, "Towards 3-D Model-Based Tracking and Recognition of Human Movement: a Multi-View Approach", International Workshop on Automatic Face- and Gesture-Recognition", pp. 272-277, 1995.
  • Rabiner, L. R. : A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)
  • T. F. Syeda-Mahmood, M. Vasilescu, and S. Sethi, "Recognizing action events from multiple viewpoints," IEEE Workshop on Detection and Recognition of Events in Video, pp. 64–72, 2001.
  • M. Z. Uddin, N. D. Thang, J. T. Kim and T. S. Kim, Human Activity Recognition Using Body Joint-Angle Features and Hidden Markov Model. ETRI Journal, vol. 33, no. 4, Aug. 2011, pp. 569-579.
  • H. Fujiyoshi and A. Lipton. Real-time human motion analysis by image skeletoniation. In IEEE Workshop on Applications of Computer Vision, pages 15-21, Princeton, 1998.
  • Y. Wang, P. Sabzmeydani, and G. Mori. Semi-latent dirichlet allocation: A hierarchical model for human action recognition. In Human Motion Workshop, (with ICCV), 2007.
  • D. Weinland, R. Ronfard, and E. Boyer, "Free viewpoint action recognition using motion history volumes," Computer Vision and Image Understanding, vol. 104, no. 2, pp. 249–257, 2006
  • J. W. Davis and A. F. Bobick, "The representation and recognition of human movement using temporal templates," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 928–934, 1997.