Online adaptive motion model-based target tracking using local search algorithm

Abstract

An adaptive tracker to address the problem of tracking objects which undergo abrupt and significant motion changes is introduced. Abrupt motion of objects is an issue which makes tracking a challenging task. To address this problem, a new adaptive motion model is proposed. The model is integrated into the sequential importance resampling particle filter (SIR PF), which is the most popular probabilistic tracking framework. In this model, in each time step, if necessary, the particles’ configurations are updated by using feedback information from the observation likelihood. In order to overcome the local-trap problem, local search algorithm with best improvement strategy is used to update particles’ configurations. Then, the motion model is updated online with respect to the configurations of the best particle in the current and previous time steps. By using this adaptive model, a more robust tracking is achieved to abrupt significant motion changes. The tracker is experimentally compared to other state-of-the-art trackers on BoBoT dataset. The experimental results confirm that the tracker outperforms the related trackers in many cases by having better PASCAL score. Furthermore, this tracker improves the accuracy of the conventional SIR PF approximately 15%.