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Abstract

Model-based 3D tracker estimate the position, rotation, and joint angles of a
given model from video data of one or multiple cameras. They often rely on
image features that are tracked over time but the accumulation of small errors
results in a drift away from the target object.
In this work, we address the drift problem for the challenging task of human
motion capture and tracking in the presence of multiple moving objects where
the error accumulation becomes even more problematic due to occlusions. To this
end, we propose an analysis-by-synthesis framework for articulated models. It
combines the complementary concepts of patch-based and region-based matching to
track both structured and homogeneous body parts. The performance of our method
is demonstrated for rigid bodies, body parts, and full human bodies where the
sequences contain fast movements, self-occlusions, multiple moving objects, and
clutter. We also provide a quantitative error analysis and comparison with
other model-based approaches.