Research On Target Tracking And Decision Algorithm Based On Deep Reinforcement Learning

In recent years,the target tracking and control algorithm based on image deep learning has made great progress in the civil and military fields.This paper focuses on reducing the target tracking loss-lock rate and improving the robustness of the gimbal control system.Aiming at the problems of target information defect,similar background interference and target discontinuous tracking in visual target tracking,an improved target tracking scheme based on trajectory information was proposed.Based on the single target tracking algorithm based on Transformer,the location of the search area on the original figure is taken as the location code to enhance the global information extraction capability of the tracker.Aiming at the similar background interference problem,the depth equalization layer algorithm is integrated to make full use of its bijection characteristics to reduce the coupling degree between target key features and background features and improve the tracking robustness.Multi-frame fusion information is used to predict the track channel,and the search area preselection mechanism in the traditional image target Tracking algorithm is improved.A Trace Transformer Single Object Tracking algorithm(TTST)with fused track information is proposed.On this basis,in view of the identity switching problem existing in multi-target Tracking tasks,a Transformer Multiple Object Tracking algorithm(TTMT)with integrated track information is proposed.The multi-target tracking problem is transformed into a single target tracking problem under the number of targets constraint,and its effectiveness is verified.based on the spatial information of the target image,a reinforcement learning model is established,and Adaptive Tracking Decisions based on Deep Q Network(DQN-ATD)is designed.By combining the motion state of the camera vehicle and the output result of the target tracking task,the reasoning and calculation of the target guidance strategy of the camera vehicle in the image space are completed.Ensure smooth completion of tracking and decision making tasks.Finally,the offline experimental environment is built to conduct joint test on target tracking task and tracking decision task.Experiments show that the Target Tracking and Decision Method(TTDM)based on deep reinforcement learning can complete the target tracking control task based on vision.