A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Human body pose detection using Bayesian spatio-temporal templates
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Dealing with Self-occlusion in Region Based Motion Capture by Means of Internal Regions
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Hierarchical Vibrations: A Structural Decomposition Approach for Image Analysis
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Exploiting spatio-temporal constraints for robust 2D pose tracking
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Nonparametric density estimation for human pose tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Computer Vision and Image Understanding
| Hi-index | 0.00 |
We present a 2Dmodel-based approach to localizing human body in images viewed from arbitrary and unknown angles. The central component is a statistical shape representation of the nonrigid and articulated body contours, where a non-linear deformation is decomposed based on the concept of parts. Several image cues are combined to relate the body configuration to the observed image, with self-occlusion explicitly treated. To accommodate large viewpoint changes, a mixture of view-dependent models is employed. Inference is done by direct sampling of the posterior mixture, using Sequential Monte Carlo (SMC) simulation enhanced with annealing and kernel move. The fitting method is independent of the number of mixture components, and does not require the preselection of a "correct" viewpoint. The models were trained on a large number of interactively labeled gait images. Preliminary tests demonstrated the feasibility of the proposed approach.