A Generalized Search Method for Multiple Competing Hypotheses in Visual Tracking

Abstract

Visual tracking frameworks have traditionally relied upon a single motion model such as Random Walk, and a fixed, embedded search method like Particle Filter. As a single motion model can't reliably handle various target motion types, the interest toward multiple motion models has grown over the years. The existence of multiple competing hypotheses or predictions by the multiple motion models opens up the possibility of a wider range of search methods. To search for the target in a fixed grid of equal sized cells, an integration of the Wang-Landau method and the Markov Chain Monte Carlo (MCMC) method has recently been introduced. In this paper, we generalize this search method to cells of variable size and location, where the cells are formed around the predictions generated by multiple motion models. The effectiveness of the proposed method is tested by adopting a multiple motion model tracker. Experiments show that the modified tracker has improved accuracy and better consistency over different runs compared to its original, and superior performance over state-of-the-art trackers in challenging video sequences.