Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (24): 152-160.DOI: 10.3778/j.issn.1002-8331.2009-0064

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Multi-frame Surveillance of Correlation Filter in UAV Object Tracking

LIN Shubin, WU Guishan, XU Jiayun, YANG Wenyuan   

  1. 1.School of Computer Science, Minnan Normal University, Zhangzhou, Fujian 363000, China
    2.Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou, Fujian 363000, China
  • Online:2021-12-15 Published:2021-12-13



  1. 1.闽南师范大学 计算机学院,福建 漳州 363000
    2.闽南师范大学 福建省粒计算及其应用重点实验室,福建 漳州 363000


Object tracking is one of the key technologies of UAV. UAV object tracking is easy to cause tracking drift or lose due to the influence of scene such as camera motion and scale change. This paper proposes an algorithm, that is multi-frame surveillance of correlation filter in UAV object tracking. By adding multiple frames of information, monitoring the change rate of the response graph according to the aberration of the view. The tracker’s recognition ability is improved effectively. The clipping matrix is used to import the real negative samples, multiple historical frames information is added to improve the robustness of the filter. The Euclidean norm is introduced to define the aberration of response graph, by supervising the change of the aberration to prevent tracking drift, the exact position of the object is obtained. According to the similarity the object model is updated. Compared with other algorithms on the UAV123 and VisDrone2019 datasets. The results show that the algorithm has favourable tracking robustness and precision in camera motion, scale change and other scenes.

Key words: computer vision, object tracking, Unmanned Aerial Vehicle(UAV), background-aware correlation filters, Euclidean norm, multi-frame surveillance



关键词: 计算机视觉, 目标跟踪, 无人机, 背景感知相关滤波, 欧几里德范数, 多帧监督