Gradual Sampling and Mutual Information Maximisation for Markerless Motion Capture

Abstract

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.