CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning to track 3D human motion from silhouettes
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D Human Motion Tracking Using Progressive Particle Filter
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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This paper presents a novel tracking algorithm, the dynamic kernel-based progressive particle filter (DKPPF), for markless 3D human body tracking. An articulated human body contains considerable degrees of freedom to be estimated. The proposed algorithm aims to reduce the computational complexity and improve the accuracy. The DKPPF decomposes the high dimensional parameter space into three low dimensional spaces and hierarchically searches the posture coefficients. Moreover, it applies multiple predictions and a mean shift tracker to estimate the human posture iteratively. A dynamic kernel model is proposed to automatically adjust the kernel bandwidth of mean shift trackers according to the probability distribution of the posture states. The kernel model is capable of improving the accuracy of the tracking result. The experimental examples show that the proposed approach can effectively improve the accuracy and expedite the computation.