High-Speed Tracking Algorithm Based on Siamese Network with Enhanced Features

In recent years, real-time object tracking technology has played an important role in many complex vision systems. As a key component, tracking algorithms have high accuracy and meet real-time requirements. SiamFC algorithm has received considerable attention because it can better balance accuracy and speed. However, the SiamFC algorithm uses a shallow backbone network, and the extracted features are difficult to cope with the complex and challenging tracking scenarios, which makes the tracker easily drift. In order to simultaneously imp-rove the tracking accuracy and speed, a high-speed tracking algorithm based on lightweight Siamese network with enhanced features is proposed. Firstly, the improved lightweight network ShuffleNetV2 is applied as the backbone network to extract features, which greatly improves the tracking speed while reducing the amount of model parameters and calculations. Secondly, a dual attention module including channel attention and spatial attention is embedded at the ends of the template branch within Siamese network, aiming at adjusting the response weights of different channels and spatial positions. Thus, the features that are useful for tracking are highlighted. Finally, the hierarchical feature fusion strategy is adopted, and the deep semantic features and shallow structure features extracted by the network are used to represent the target from multiple angles. Experimental results show that the proposed algorithm has greater advantages in tracking accuracy and stronger robustness in difficult scenarios in comparison with some current outstanding tracking algorithms on OTB100 and VOT2018 datasets. At the same time, the algo-rithm speed can reach 110 FPS under NVIDIA GTX1070, which can better balance tracking accuracy and speed in comparison with SiamFC algorithm.