Light-weight UAV object tracking network based on strategy gradient and attention mechanism

Abstract

Most existing object tracking methods have poor adaptability to complex scenes, and cannot achieve a good balance between tracking accuracy and real-time performance. To solve the above problems, this paper proposes a lightweight UAV object real-time tracking algorithm based on strategy gradient and attention. Firstly, a lightweight E-Mobile Net is designed as the backbone network of feature extraction; secondly, a feature enhanced attention assistant module is designed to enhance the adaptability and discrimination ability of the model; with multi-layer feature fusion regional suggestion network, foreground background classification and boundary box regression response map are obtained by cross-correlation, and the tracking results are calculated. The strategy network based on strategy gradient is used to optimize the template update and re detection strategy, which improves the overall tracking accuracy and efficiency of the model. Simulation experiments on an embedded device and multiple standard data sets show that compared with the current mainstream algorithms, the tracking accuracy is significantly improved 20%∼30%, the algorithm robustness also has obvious advantages, and the tracking speed on an embedded device is 56 fps can meet the real-time requirements.