Enhanced Initialization with MultiStage Learning for Robust Visual Tracking Visual tracking is the task of estimating the trajectory of a target given its first location in a video. Existing trackingbydetection approaches build trackers on binary classifiers. However, these approaches are sometimes not discriminative enough to distinguish the target from the background, which impedes the performance under challenging conditions. In this paper, we demonstrate that conventional initializing strategies make the model insufficiently trained. To make full use of the information contained in the first frame, we propose an enhanced scheme of model initialization. In the scheme, several kinds of negative samples are heuristically defined and a multistage learning mechanism is adopted to make the model initialization more efficient and stable. Validation experiment demonstrates the effectiveness of the mechanism. Experiments on benchmark datasets show that the proposed method improves the results of the baseline work and achieves stateoftheart performance.

Enhanced Semantic Features via Attention for RealTime Visual Tracking The key to balance the tracking accuracy and speed for object tracking algorithms is to learn powerful features via offline training in a lightweight tracking framework. With the development of attention mechanisms, itxe2x80x99s facile to apply attention to enhance the features without modifying the basic structure of the network. In this paper, a novel combination of different attention modules is implemented into a siamesebased tracker and boosts the tracking performance with little computational burden. In particular, by applying nonlocal selfattention and dual pooling channel attention, the extracted features tend to be more discriminative and adaptive due to the offline learning with tracking targets of different classes. Meanwhile, an IndexDifferenceweight boosts the performance and reduces overfitting when full occlusion occurs. Our experimental results on OTB2013 and OTB2015 show that the tracker using the proposal to implement the attention modules can achieve stateoftheart performance with a speed of 49 frames per second.