CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Tracking persons in monocular image sequences
Computer Vision and Image Understanding
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Twist Based Acquisition and Tracking of Animal and Human Kinematics
International Journal of Computer Vision
A 3d dynamic model of human actions for probabilistic image tracking
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences
EURASIP Journal on Advances in Signal Processing - Image processing and analysis in biomechanics
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In this paper we present a technique for predicting the 2D human body joints and limbs position in monocular image sequences, and reconstructing its corresponding 3D postures using information provided by a 3D action model. This method is used in a framework based on particle filtering, for the automatic tracking and reconstruction of the 3D human body postures. A set of the reconstructed postures up to time t are projected on the action space defined in this work, which is learnt from Motion Capture data, and provides us a principled way to establish similarity between body postures, natural occlusion handling, invariance to viewpoint, robustness, and is able to handle different people and different speeds while performing an action. Results on manually selected joint positions on real image sequences are shown in order to prove the correctness of this approach.