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
Walking Appearance Manifolds without Falling Off
Neural Information Processing
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
Image and Vision Computing
Learning Generative Models for Multi-Activity Body Pose Estimation
International Journal of Computer Vision
A swarm-intelligence based algorithm for face tracking
International Journal of Intelligent Systems Technologies and Applications
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
Dynamic tracking system for object recognition
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Learning generative models for monocular body pose estimation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multi-activity tracking in LLE body pose space
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
Manifold topological multi-resolution analysis method
Pattern Recognition
Robust auxiliary particle filter with an adaptive appearance model for visual tracking
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
What can we learn from biological vision studies for human motion segmentation?
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Dynamic appearance is one of the most important cues for tracking and identifying moving people. However, direct modeling spatio-temporal variations of such appearance is often a difficult problem due to their high dimensionality and nonlinearities. In this paper we present a human tracking system that uses a dynamic appearance and motion modeling framework based on the use of robust system dynamics identification and nonlinear dimensionality reduction techniques. The proposed system learns dynamic appearance and motion models from a small set of initial frames and does not require prior knowledge such as gender or type of activity. The advantages of the proposed tracking system are illustrated with several examples where the learned dynamics accurately predict the location and appearance of the targets in future frames, preventing tracking failures due to model drifting, target occlusion and scene clutter.