A SiameseDetection Network for RealTime Object Tracking Siamese networks recently have caused great attention in video tracking community. It successfully achieves not only realtime tracking, but also high precision. However, the typical Siamese networks are relatively mean performance to the target of scale variation, rapid deformation and etc due to the operation on the central point of the object only, rather than the entire spatial information. In this paper, we propose a spatialaware video tracking method called SiamRFC to predict the central point of the tracked object and estimate its spatial configuration simultaneously. Specifically, our SiamRFC firstly predicts the central point of tracked object via Siamese network by judging the similarity. According to this central, we further estimate its optimal anchor configuration of the tracked object among multiple candidates, and then based on the optimal anchor to regress target position. Extensive evaluations on VOT2015, VOT2016 and VOT2017 benchmarks demonstrate that the proposed SiamRFC tracker is competitive with stateoftheart trackers.

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.