CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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 Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Human body pose estimation with particle swarm optimisation
Evolutionary Computation
Human identification based on gait paths
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Differential evolution based human body pose estimation from point clouds
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Generative tracking of 3D human motion in latent space by sequential clonal selection algorithm
Multimedia Tools and Applications
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This paper proposes the use of a particle filter with embedded particle swarm optimization as an efficient and effective way of dealing with 3d model-based human body tracking. A particle swarm optimization algorithm is utilized in the particle filter to shift the particles toward more promising configurations of the human model. The algorithm is shown to be able of tracking full articulated body motion efficiently. It outperforms the annealed particle filter, kernel particle filter as well as a tracker based on particle swarm optimization. Experiments on real video sequences as well as a qualitative analysis demonstrate the strength of the approach.