PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Monocular human motion capture with a mixture of regressors
William Triggs and Ankur Agarwal
In: CVPR 2005, 20-26 June 2005, San Diego, California.


We address 3D human motion capture from monocular images, taking a learning based approach to construct a probabilistic pose estimation model from a set of labelled human silhouettes. To compensate for ambiguities in the pose reconstruction problem, our model explicitly calculates several possible pose hypotheses. It uses locality on a manifold in the input space and connectivity in the output space to identify regions of multi-valuedness in the mapping from silhouette to 3D pose. This information is used to fit a mixture of regressors on the input manifold, giving us a global model capable of predicting the possible poses with corresponding probabilities. These are then used in a dynamical-model based tracker that automatically detects tracking failures and re-initializes in a probabilistically correct manner. The system is trained on optical sensor based motion capture data, using the corresponding real human silhouettes supplemented with silhouettes synthesized artificially from several different models for improved robustness to inter-person variations. Static pose estimation is illustrated on a variety of silhouettes. The robustness of the method is demonstrated by tracking on a real image sequence requiring multiple automatic re-initializations.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:799
Deposited By:William Triggs
Deposited On:30 December 2004