Hybrid Particle Filter and Mean Shift tracker with adaptive transition model

We propose a tracking algorithm based on a combination of particle filter and mean shift, and enhanced with a new adaptive state transition model. The particle filter is robust to partial and total occlusions, can deal with multi-modal pdf and can recover lost tracks. However, its complexity dramatically increases with the dimensionality of the sampled pdf. Mean shift has a low complexity, but is unable to deal with multi-modal pdf. To overcome these problems, the proposed tracker first produces a smaller number of samples than the particle filter and then shifts the samples toward a close local maximum using mean shift. The transition model predicts the state based on adaptive variances. Experimental results show that the combined tracker outperforms the particle filter and mean shift in terms of accuracy in estimating the target size and position while generating 80% less samples than the particle filter.