Tracker-Level Fusion for Robust Bayesian Visual Tracking

We propose a tracker-level fusion framework for robust visual tracking. The framework combines trackers addressing different tracking challenges to improve the overall performance. A novelty of the proposed framework is the inclusion of an online performance measure to identify the track quality level of each tracker so as to guide the fusion. The fusion is then based on appropriately mixing the prior state of the trackers. Moreover, the track-quality level is used to update the target appearance model. We demonstrate the framework with two Bayesian trackers on video sequences with various challenges and show its robustness compared with the independent use of the two individual trackers, and also compared with state-of-the-art trackers that use tracker-level fusion.