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Abstract:
Tracking of human motion in video is usually tackled either
by local optimization or filtering approaches. While
local optimization offers accurate estimates but often looses
track due to local optima, particle filtering can recover from
errors at the expense of a poor accuracy due to overestimation
of noise. In this paper, we propose to embed global
stochastic optimization in a tracking framework. This new
optimization technique exhibits both the robustness of filtering
strategies and a remarkable accuracy. We apply the
optimization to an energy function that relies on silhouettes
and color, as well as some prior information on physical
constraints. This framework provides a general solution to
markerless human motion capture since neither excessive
preprocessing nor strong assumptions except of a 3D model
are required. The optimization provides initialization and
accurate tracking even in case of low contrast and challenging
illumination. Our experimental evaluation demonstrates
the large improvements obtained with this technique.
It comprises a quantitative error analysis comparing the
approach with local optimization, particle filtering, and a
heuristic based on particle filtering.
