Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A survey of advances in vision-based human motion capture and analysis
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Human body pose estimation with particle swarm optimisation
Evolutionary Computation
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International Journal of Computer Vision
Markerless articulated human body tracking from multi-view video with GPU-PSO
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
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In this paper, we present a new adaptive approach to multi-view markerless articulated human body pose estimation from multi-view video sequences, using Particle Swarm Optimisation (PSO). We address the computational complexity of the recently developed hierarchical PSO (HPSO) approach, which successfully estimated a wide range of different motion with a fixed set of parameters, but incurred an unnecessary overhead in computational complexity. Our adaptive approach, called APSO, preserves the black-box property of the HPSO in that it requires no parameter value input from the user. Instead, it adaptively changes the value of the search parameters online, depending on the quality of the pose estimate in the preceding frame of the sequence. We experimentally compare our adaptive approach with HPSO on four different video sequences and show that the computational complexity can be reduced without sacrificing accuracy and without requiring any user input or prior knowledge about the estimated motion type.