Influence of the observation likelihood function on particle filtering performance in tracking applications

Since the introduction of particle filtering for object tracking, a lot of improvements have been suggested. However, the definition of the observation likelihood function, needed for determining the particle weights, has received little attention. Because particle weights determine how the particles are re-sampled, the likelihood function has a strong influence on the tracking performance. We show experimental results for three different tracking tasks for different parameter values of the assumed observation model. The results show a large influence of the model parameters on the tracking performance. Optimizing the likelihood function can give significant tracking improvement. Different optimal parameter settings are observed for the three different tracking tasks. Consequently, when performing multiple tasks a trade-off must be made for the parameter setting. In practical situations where robust tracking must be achieved with a limited amount of particles, the true observation probability is not always the optimal likelihood function.