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Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (18): 28334-028334.doi: 10.7527/S1000-6893.2023.28334

• Reviews •    

Research progress of UAV aerial video multi⁃object detection and tracking based on deep learning

Yubin YUAN, Yiquan WU(), Langyue ZHAO, Jinlin CHEN, Qichang ZHAO   

  1. College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2022-11-29 Revised:2022-12-31 Accepted:2023-03-16 Online:2023-03-23 Published:2023-03-21
  • Contact: Yiquan WU E-mail:nuaaimage@163.com
  • Supported by:
    National Natural Science Foundation of China(61573183)

Abstract:

With the increasing convenience of data acquisition for aerial photography of Unmanned Aerial Vehicle (UAV), the multi-target detection and tracking technology based on the UAV platform has developed rapidly and has broad prospects for applications in civil and military fields. In recent years, the rapid progress of in-depth learning has also provided a variety of more effective solutions. However, the challenging problems such as sudden changes in the appearance of the target, serious occlusion of the target area, and disappearance and reappearance of the target from the perspective of UAV have not been completely solved. In this paper, we summarize the algorithms for multi-target detection and tracking in UAV aerial video based on deep learning, and summarize the latest progress in this field, including multi-target detection and multi-object tracking. The multi-object detection module is divided into two parts: two-stage and one-stage detection. For the multi-object tracking module, according to the two classical frameworks of tracking-based detection and joint-detection tracking, the principles of the two algorithms are described and their advantages and disadvantages are analyzed. Then, the existing public data sets are statistically analyzed, and the optimal schemes of the benchmark challenge VisDrone Challenge in the field of multi-target detection and tracking based on UAV aerial video in recent years are compared and analyzed. Finally, the paper discusses the urgent problems of multi-object detection and tracking from the perspective of UAV and the possible research directions in the future, providing a reference for the follow-up researchers.

Key words: UAV aerial video, multi object detection, multi target tracking, deep learning, one-stage detection, two-stage detection, detection tracking, joint-detection tracking

CLC Number: