Multipath Based Correlation Filter for Visual Object Tracking

This paper presents a new correlation filter based visual object tracking method to improve the accuracy and robustness of trackers. Most of the current correlation filter based tracking methods often suffer in situations such as fast object motion, the presence of similar objects, partial or full occlusion. One of the reasons for that is that object localization is performed by selecting only a single location at each frame (greedy search technique). Instead of choosing a single position, the multipath based tracking method considers multiple locations in each frame to localize object position accurately. In this paper, the multipath based tracking method is applied to improve the performance of the efficient convolution operator with handcrafted features (ECO-HC), which is a top performing tracker in many visual tracking datasets. We have performed comprehensive experiments using our efficient convolution operator with multipath (ECO-MPT) tracker on UAV123@10fps and UAV20L datasets. We have shown that our tracker outperforms most of the state-of-art trackers in all those benchmark datasets.