Learning a Novel Ensemble Tracker for Robust Visual Tracking

In this article, we propose a novel historical snapshot-based ensemble tracker (HSET) to address visual object tracking. Specifically, our HSET tracker collects multiple historical tracker snapshots to model various appearance patterns of the target object during tracking, and performs ensemble operations based on these tracker snapshots to successfully detect the target object. To obtain diverse and representative tracker snapshots for ensemble tracking, we design a tracker snapshot verification scheme to handle dynamical appearance variations of the target object and alleviate unreliable tracker snapshots. Furthermore, the weights of different tracker snapshots are given by an online weight assign algorithm with consideration of both historical appearance information and recent appearance information of the target object. By employing ensemble learning and historical tracker snapshots, the proposed HSET method can get impressive generalization power and tracking robustness to handle significant appearance changes and model drift. Extensive experimental results on public tracking benchmarks indicate that the proposed HSET tracking algorithm reaches encouraging tracking performance compared to multiple state-of-the-art tracking algorithms.