Learning a Reliable Decision Making Policy for Robust Tracking Recent years deep learning based visual object trackers have achieved stateoftheart performance on multiple benchmarks. However, most of these trackers lack an effective mechanism to avoid the wrong template update or redetect the object when unreliable tracking result appears. In this paper, a novel tracking framework consisting of a tracking network for locating the target and a policy network for decision making is proposed. Firstly, during the offline training phase, a variant of policy gradient algorithm is adopted, which makes the model converge better and faster. Secondly, current response map and history response map are both fed to the policy network to check the reliability of the tracking result, which effectively distinguishes the response diversity. Finally, an efficient redetection module is proposed to filter a large number of searching areas, which greatly improves the speed. Our proposed algorithm is measured on OTB dataset. Assessment results show that our tracking algorithm improves performance by 5%6% at the expense of only a small amount of speed.

Benchmarking Deep Trackers on Aerial Videos In recent years, deep learningbased visual object trackers have achieved stateoftheart performance on several visual object tracking benchmarks. However, most tracking benchmarks are focused on ground level videos, whereas aerial tracking presents a new set of challenges. In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. In our experiments, we use a subset of OTB2015 dataset with aerial style videos; the UAV123 dataset without synthetic sequences; the UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our benchmark datasets. We compare the advantages and disadvantages of different trackers in different tracking situations encountered in aerial data. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos. We attribute this effect to smaller target size, camera motion, significant camera rotation with respect to the target, out of view movement, and clutter in the form of occlusions or similar looking distractors near tracked object.