Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Articulated object registration using simulated physical force/moment for 3D human motion tracking
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
VACE multimodal meeting corpus
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
| Hi-index | 0.00 |
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 [3,4,5]. 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.