Learned Models for Estimation of Rigid and ArticulatedHuman Motion from Stationary or Moving Camera
International Journal of Computer Vision
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Recovering Articulated Pose: A Comparison of Two Pre and Postimposed Constraint Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
A recursive approach to the design of adjustable linear models for complex motion analysis
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
A recursive approach to the design of adjustable linear models for complex motion analysis
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Complex motion models for simple optical flow estimation
Proceedings of the 32nd DAGM conference on Pattern recognition
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An approach for learning and estimating temporal-flow models from image sequences is proposed. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are also developed for modeling the motion of regions in rigid and coordinated motion. The performance of these models is demonstrated on several long image sequences of rigid and articulated bodies in motion.