Likelihood tuning for particle filter in visual tracking

Particle filters (PF) are widely used in the vision literature for visual object tracking. However, the selection and the tuning of the observation PDF (or likelihood function) involved in the particle weighting stage are often eclipsed. These considerations have a strong influence on the tracking performance, especially for human motion capture (HMC) due to the high number of degrees of freedom and the presence of local extrema in the state space. The proposed method is illustrated in the HMC context on a predefined set of likelihoods and assessed w.r.t. a ground truth provided by a commercial HMC system. This paper highlights the influence of their associated free parameters as well as their combination in order to characterize the optimal unified likelihood function. These insights lead to some heuristics to tackle the difficult problem of the likelihood function tuning.