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.

Efficient Dual Attention Module for RealTime Visual Tracking Attention mechanisms are of great potential to enhance discriminative capacity and adaptability of Convolutional Neural Networks. However, due to speed requirements of trackers, it is inappropriate to introduce attention modules that need high computationalcost into tracking algorithms. To employ attention mechanisms in tracking field, we propose Efficient Dual Attention Module (EDAM) which is a lightweight and effective module to extract attention in channel and spatial dimensions. Firstly, we aggregate spatial and channel information with selfattention mechanism by a simplified Nonlocal Network, which is called Attentionwise Global Pooling. Next, we extract dual attention by modeling interchannel and interposition relationships and finally fuse them in a sequential manner. With the use of dual attention, the network adaptively attends to discriminative and robust features of targets. Extensive experiments on a standard tracking benchmark demonstrate that our tracker with an EDAM embedded achieves favorable performance against stateoftheart trackers while runs at realtime speed.