TY - GEN
T1 - Articulated body tracking using dynamic belief propagation
AU - Han, Tony X.
AU - Huang, Thomas S.
PY - 2005
Y1 - 2005
N2 - An efficient articulated body tracking algorithm is proposed in this paper. Due to the high dimensionality of human-body motion, current articulated tracking algorithms based on sampling [1], belief propagation (BP) [2], or non-parametric belief propagation (NBP) [3], are very slow. To accelerate the articulated tracking algorithm, we adapted belief propagation according to the dynamics of articulated human motion. The searching space is selected according to the prediction based on human motion dynamics and current body-configuration estimation. The searching space of the dynamic BP tracker is much smaller than the one of traditional BP tracker [2] and the dynamic BP need not the slow Gibbs sampler used in NBP [3-5]. Based on a graphical model similar to the pictorial structure [6] or loose-limbed model [3], the proposed efficient, dynamic BP is carried out to find the MAP of the body configuration. The experiments on tracking the body movement in meeting scenario show robustness and efficiency of the proposed algorithm.
AB - An efficient articulated body tracking algorithm is proposed in this paper. Due to the high dimensionality of human-body motion, current articulated tracking algorithms based on sampling [1], belief propagation (BP) [2], or non-parametric belief propagation (NBP) [3], are very slow. To accelerate the articulated tracking algorithm, we adapted belief propagation according to the dynamics of articulated human motion. The searching space is selected according to the prediction based on human motion dynamics and current body-configuration estimation. The searching space of the dynamic BP tracker is much smaller than the one of traditional BP tracker [2] and the dynamic BP need not the slow Gibbs sampler used in NBP [3-5]. Based on a graphical model similar to the pictorial structure [6] or loose-limbed model [3], the proposed efficient, dynamic BP is carried out to find the MAP of the body configuration. The experiments on tracking the body movement in meeting scenario show robustness and efficiency of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=33646690008&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646690008&partnerID=8YFLogxK
U2 - 10.1007/11573425_3
DO - 10.1007/11573425_3
M3 - Conference contribution
AN - SCOPUS:33646690008
SN - 3540296204
SN - 9783540296201
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 26
EP - 35
BT - Computer Vision in Human-Computer Interaction - ICCV 2005 Workshop on HCI, Proceedings
T2 - ICCV 2005 Workshop on HCI - Computer Vision in Human-Computer Interaction
Y2 - 21 October 2005 through 21 October 2005
ER -