3D Model-Based Tracking of Humans in Action: A Multi-View Approach.
MARYLAND UNIV COLLEGE PARK COMPUTER VISION LAB
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We present a vision system for the 3D model-based tracking of unconstrained human movement. Using image sequences acquired simultaneously from multiple views, we recover the 3D body pose at each time instant without the use of markers. The pose-recovery problem is formulated as a search problem and entails finding the pose parameters of a graphical human model whose synthesized appearance is most similar to the actual appearance of the real human in the multi-view images. The models used for this purpose are acquired from the images. We use a decomposition approach and a best-first technique to search through the high dimensional pose parameter space. A robust variant of chamfer matching is used as a fast similarity measure between synthesized and real edge images. We present initial tracking results from a large new Humans-In-Action HIA database containing more than 2500 frames in each of four orthogonal views. The four image streams are synchronized. They contain subjects involved in a variety of activities, of various degrees of complexity, ranging from simple one-person hand waving to two-person close interaction in the Argentine tango.
- Anatomy and Physiology