ROBUST ONLINE VISUAL TRACKING VIA A TEMPORAL ENSEMBLE FRAMEWORK

In this paper, we propose a robust visual tracking method based on a temporal ensemble framework. Different from conventional ensemble-based trackers, which combine weak classifiers into a strong one using AdBoost in spatial fusion manners, our method adopts a powerful and efficient tracker integrated with its snapshots in different temporal windows of online tracking process to construct a temporal ensemble framework. Specifically, an adaptive correlation filter classifier is employed as the base tracker. During online tracking, the ensemble model determines the output through fusion of the base tracker's snapshots based on their response scores. By the temporal ensemble, accumulated errors caused by undesirable update can be corrected which greatly improves the robustness of the tracking system. Encouraging experimental results on challenging benchmark video sequences demonstrate that the proposed tracking method outperforms several state-of-the-art trackers in terms of both precision and robustness.