Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target’s position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target’s position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

由于背景场景的复杂性和目标外观的变化，很难实现高精度和快速的目标跟踪。当前，基于相关过滤器的跟踪器（CFT）在对象跟踪方面显示出令人鼓舞的性能。CFT通过具有不同特征的相关滤波器估计目标的位置。但是，在长期跟踪漂移的情况下，大多数CFT几乎无法重新检测目标。在本文中，提出了一种名为相关过滤器和在线学习（CFOL）的特征集成对象跟踪器。CFOL使用具有多种功能的相同判别相关滤波器来估计目标的位置及其相应的相关分数。为了减少跟踪漂移，提出了一种新的在线学习采样和更新策略。