Abstract
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The major issue in markerless motion capture is finding the
global optimum from the multimodal setting where distinctive gestures
may have similar likelihood values. Instead of only focusing on effective
searching as many existing works, our approach resolves gesture ambiguity
by designing a better-behaved observation likelihood. We extend
Annealed Particle Filtering by a novel gradual sampling scheme that
allows evaluations to concentrate on large mismatches of the tracking
subject. Noticing the limitation of silhouettes in resolving gesture ambiguity,
we incorporate appearance information in an illumination invariant
way by maximising Mutual Information between an appearance
model and the observation. This in turn strengthens the effectiveness of
the better-behaved likelihood. Experiments on the benchmark datasets
show that our tracking performance is comparable to or higher than the
state-of-the-art studies, but with simpler setting and higher computational
efficiency.