Abstract:
This paper presents a novel approach of Human Activity Recognition (HAR) using the joint angles of the human body in 3-D. From each pair of activity video images acquired...Show MoreMetadata
Abstract:
This paper presents a novel approach of Human Activity Recognition (HAR) using the joint angles of the human body in 3-D. From each pair of activity video images acquired by a stereo camera, the body joint angles are estimated by co-registering a 3-D body model to the stereo information: our approach uses no attached sensors on the human. The estimated joint angle features from the time-sequential activity video frames are then mapped into codewords to generate a sequence of discrete symbols for a Hidden Markov Model (HMM) of each activity. With these symbols, each activity HMM is trained and used for activity recognition. The performance of our joint angle-based HAR has been compared to that of the conventional binary silhouette-based HAR, producing significantly better results in the recognition rate: especially for those activities that are not discernible with the conventional approaches.
Published in: 2010 IEEE International Conference on Image Processing
Date of Conference: 26-29 September 2010
Date Added to IEEE Xplore: 03 December 2010
ISBN Information:
ISSN Information:
References is not available for this document.
Contents Select All
1.
F. Niu and M. Abdel-Mottaleb, "View-Invariant Human Activity Recognition Based on Shape and Motion Features," in Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering, pp. 546-556, 2004.
2.
M. Z. Uddin, J. J. Lee, and T.-S. Kim, "Independent Shape Component-Based Human Activity Recognition via Hidden Markov Model," Applied Intelligence, DOI:10.1007/s10489-008-0159-2, 2009.
3.
M. Z. Uddin, J. J. Lee and T.-S. Kim, "Human Activity Recognition Using Independent Component Features from Depth Images," in Proceedings of the 5th International Conference on Ubiquitous Healthcare, pp. 181-183, 2008.
4.
N. D. Thang, Y.-K Lee, S.-Y. Lee, and T.-S. Kim, "Estimation of 3-D Human Body Posture via Co-Registration of 3-D Human Model and Sequential Stereo Information," Applied Intelligence, DOI: 10.1007/s10489-009-0209-4, 2010.
5.
J. Cech and R. Sara, "Efficient sampling of disparity space for fast and accurate matching," in Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
6.
Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23(11), pp. 1222-1239, 2001.
7.
P. Viola and M. J. Jones, "Robust real-time face detection," International Journal of Computer Vision, Vol. 57(2), pp. 137-154, 2004.
