Optoelectronics Letters, Volume. 19, Issue 2, 105(2023)

Multi-object tracking based on deep associated features for UAV applications

Lingyu XIONG and Guijin TANG*
Author Affiliations
  • School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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    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.

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    XIONG Lingyu, TANG Guijin. Multi-object tracking based on deep associated features for UAV applications[J]. Optoelectronics Letters, 2023, 19(2): 105

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    Paper Information

    Category: Image and Information processing

    Received: Apr. 24, 2022

    Accepted: Oct. 17, 2022

    Published Online: Mar. 18, 2023

    The Author Email: Guijin TANG (tanggj@njupt.edu.cn)

    DOI:10.1007/s11801-023-2070-9

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