On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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 Model-Based Approach for Estimating Human 3D Poses in Static Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
GPU-accelerated tracking of the motion of 3D articulated figure
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Real-time multi-view human motion tracking using particle swarm optimization with resampling
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
View independent human gait recognition using markerless 3d human motion capture
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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We investigate swarm intelligence based searching schemes for effective articulated human body tracking. The fitness function is smoothed in an annealing scheme and then quantized. This allows us to extract a pool of candidate best particles. The algorithm selects a global best from such a pool. We propose a global-local annealed particle swarm optimization to alleviate the inconsistencies between the observed human pose and the estimated configuration of the 3D model. At the beginning of each optimization cycle, estimation of the pose of the whole body takes place and then the limb poses are refined locally using smaller number of particles. The investigated searching schemes were compared by analyses carried out both through qualitative visual evaluations as well as quantitatively through the use of the motion capture data as ground truth. The experimental results show that our algorithm outperforms the other swarm intelligence searching schemes. The images were captured using multi-camera system consisting of calibrated and synchronized cameras.