Symbiotic Tracker Ensemble Toward A Unified Tracking Framework

Tracking people and objects is a fundamental stage toward many video surveillance systems, for which various trackers have been specifically designed in the past decade. However, it comes to a consensus that there is not any specific tracker that works sufficiently well under all circumstances. Therefore, one potential solution is to deploy multiple trackers, with a tracker output fusion step to boost the overall performance. Subsequently, an intelligent fusion design, yet general and orthogonal to any specific tracker, plays a key role in successful tracking. In this paper, we propose a symbiotic tracker ensemble toward a unified tracking framework, which is based on only the output of each individual tracker, without knowing its specific mechanism. In our approach, all trackers run in parallel, without requiring any details for tracker running, which means that all trackers are treated as black boxes. The proposed symbiotic tracker ensemble framework aims at learning an optimal combination of these tracking results. Our method captures the relation among individual trackers robustly from two aspects. First, the consistency between two successive frames is calculated for each tracker. Then, the pair-wise correlation among different trackers is estimated in the new coming frame by a graph-propagation process. Experimental results on the Caremedia dataset and the Caviar dataset demonstrate the effectiveness of the proposed method, with comparisons to several state-of-the-art methods.