An incremental Bhattacharyya dissimilarity measure for particle filtering

The dissimilarity between a target descriptor and a particle descriptor is a crucial parameter in the particle filtering (PF), while the widely used Bhattacharyya dissimilarity (BD) is not discriminative enough. This paper presents an incremental Bhattacharyya dissimilarity (IBD) for measuring histogram based descriptors (HBDs) used for particle weight estimation. IBD is defined by incorporating an incremental similarity matrix (ISM) into the BD. Such an ISM imposes the incremental similarity beliefs on the matched bin patches of two input histograms and enables a cross-bin interaction during the comparison, which yields the enhanced capability of discriminating the particles located in the object from those positioned in the background. We propose a robust approach to compute the ISM by jointly utilizing the spatial and temporal attributes. Also, to handle target appearance changes and deformations, a classification-inspired target model update strategy is presented. These components lead to an effective and robust tracking algorithm. Experimental results demonstrate that IBD shows promising discriminative capability in comparison with other state of the art dissimilarity measures. Moreover, the IBD based PF-tracker also exhibits competitive tracking performance, especially under scenarios of partial occlusion and background clutter.