Improved Siamese Adaptive Network and Multi-feature Fusion Tracking Algorithm

Abstract

Aiming at the problem that tracking accuracy and tracking speed are difficult to balance in the current target tracking field. For example, a tracker based on correlation filtering can run at a very high speed, but the tracking accuracy is extremely low; a tracker based on deep learning can achieve high tracking accuracy, but the tracking speed is extremely low. On this basis, an improved Siamese adaptive network and multi-feature fusion target tracking algorithm are proposed. Firstly, the AlexNet network and the improved ResNet network are constructed on each branch of the Siamese network at the same time for feature extraction. Secondly, through end-to-end training at the same time, the tracking problem is decomposed into sub-problems of classifying each position label and returning to the bounding box. Finally, the shallow features and deep features are selected adaptively, and the target recognition and location are carried out based on multi-feature fusion. The proposed algorithm and some existing trackers are tested on the target tracking standard dataset. Experimental results show that the proposed algorithm can achieve high target tracking accuracy and success rate while ensuring tracking speed. At the same time, the algorithm has strong robustness in complex situations such as illumination changes, deformations, and background clutter.