Bayesian Fusion of Back Projected Probabilities (BFBP): Co-occurrence Descriptors for Tracking in Complex Environments

Abstract

Among the multitude of probabilistic tracking techniques, the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm has been one of the most popular. Though several modifications have been proposed to the original formulation of CAMSHIFT, limitations still exist. In particular the algorithm underperforms when tracking textured and patterned objects. In this paper we generalize CAMSHIFT for the purposes of tracking such objects in non-stationary backgrounds. Our extension introduces a novel object modeling technique, while retaining a probabilistic back projection stage similar to the original CAMSHIFT algorithm, but with considerably more discriminative power. The object modeling now evolves beyond a single probability distribution to a more generalized joint density function on localized color patterns. In our framework, multiple co-occurrence density functions are estimated using information from several color channel combinations and these distributions are combined using an intuitive Bayesian approach. We validate our approach on several aerial tracking scenarios and demonstrate its improved performance over the original CAMSHIFT algorithm and one of its most successful variants.