Uncertain motion tracking based on convolutional net with semantics estimation and region proposals Abstract   In real world, most of the tracking methods suffer from uncertain motion, which may make the tracker failure because of a local search window with the motion smooth assumption. To address this problem, a novel tracking framework based on convolutional net with semantics estimation and region proposals is proposed. Firstly, we present a semantics object proposals generation strategy, including categorylevel semantics proposals, oneobjectlevel semantics estimation and semanticscontextual proposals generation, to obtain a few of highquality objectoriented proposals covering uncertain motion. Secondly, combining the globally sparse semantics region proposals prediction and correlation filter prediction, a hybrid semantics tracking algorithm is proposed, which obtains a coarse object location by the decision of multiple response maps. Finally, we learn and train independent correlation filter to estimate the scale of target for a higher tracking accuracy. Extensive experiments on two visual tracking benchmarks and results demonstrate our method achieves stateoftheart performance.

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.