Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Kernel Particle Filter for Real-Time 3D Body Tracking in Monocular Color Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A Modular Approach to the Analysis and Evaluation of Particle Filters for Figure Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Monocular body pose estimation by color histograms and point tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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Particle filter is widely used in human motion tracking but its efficiency is low. A contour cue based particle filter algorithm is proposed in this paper for the human motion tracking in a markerless monocular video. The likelihood of sampled particles is measured by chamfer distance between two contours. One contour is extracted from the video image. The other is transformed from the sampled particle. The value of likelihood is the weight of corresponding particle. Then weighted particles are optimized by Levenberg-Marquardt method to make the final estimation closer to the posterior distribution of motion state. Apart from this, the skin part of human body is detected to constrain the sampled particles when the contour feature points are not sufficient with large occlusion. The experiment result shows that the contour cue based method is more efficient than the edge method.