Gradual sampling and mutual information maximisation for markerless motion capture

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.