Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
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
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
Markerless human articulated tracking using hierarchical particle swarm optimisation
Image and Vision Computing
Swarm intelligence based searching schemes for articulated 3D body motion tracking
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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This paper proposes a new algorithm called soft partitioning particle swarm optimization (SPPSO), which performs video-based markerless human pose tracking by optimizing a fitness function in a 31-dimensional search space. The fitness function is based on foreground segmentation and edges. SPPSO divides the optimization into two stages that exploit the hierarchical structure of the model. The first stage only optimizes the most important parameters, whereas the second is a global optimization which also refines the estimates from the first stage. Experiments with the publicly available Lee walk dataset showed that SPPSO performs better than the annealed particle filter at a frame rate of 20 fps, and equally well at 60 fps. The better performance at the lower frame rate is attributed to the explicit exploitation of the hierarchical model structure.