Twist Based Acquisition and Tracking of Animal and Human Kinematics
International Journal of Computer Vision
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
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Monocular 3D tracking of articulated human motion in silhouette and pose manifolds
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Human body pose estimation with particle swarm optimisation
Evolutionary Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Gaussian processes for human tracking and action classification
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Multi-activity tracking in LLE body pose space
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Markerless human articulated tracking using hierarchical particle swarm optimisation
Image and Vision Computing
Charting-based subspace learning for video-based human action classification
Machine Vision and Applications
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We present a framework for markerless articulated human motion tracking in multi-view sequences. We learn motion models of common actions in a low-dimensional latent space using charting, a nonlinear dimensionality reduction took which estimates automatically the dimension of the latent space and keeps similar poses close together in it. Additionally charting obtains the inverse mapping from the low dimensional latent space to the high-dimensional joint angle space. The tracking is formulated as a low-dimensional nonlinear optimisation in the latent space and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve di±cult nonlinear optimisation problems. Tracking results with the walking, kicking, praying, posing and punch sequences demonstrate the good accuracy and performance of our approach