Abstract
We present a vision system for the 3-D model-based tracking of
unconstrained human movement. Using image sequences acquired
simultaneously from multiple views, we recover the 3-D 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. They contain subjects involved in a variety of
activities, of various degrees of complexity, ranging from the more
simple one-person hand waving to the challenging two-person close
interaction in the Argentine Tango
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