XIONG Lingyu,TANG Guijin.Multi-object tracking based on deep associated features for UAV applications[J].Optoelectronics Letters,2023,(2):105-111 Multi-object tracking based on deep associated features for UAV applications Author Name Affiliation XIONG Lingyu  School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China  TANG Guijin  School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China  Abstract:       Multi-object tracking (MOT) techniques have been increasingly applied in a diverse range of tasks. Unmanned aerial vehicle (UAV) is one of its typical application scenarios. Due to the scene complexity and the low resolution of moving targets in UAV applications, it is difficult to extract target features and identify them. In order to solve this problem, we propose a new re-identification (re-ID) network to extract association features for tracking in the association stage. Moreover, in order to reduce the complexity of detection model, we perform the lightweight optimization for it. Experimental results show that the proposed re-ID network can effectively reduce the number of identity switches, and surpass current state-of-the-art algorithms. In the meantime, the optimized detector can increase the speed by 27% owing to its lightweight design, which enables it to further meet the requirements of UAV tracking tasks. Hits: 274 Download times: 0 View Full Text    Download reader