Scale-Adaptive Vehicle Tracking Algorithm in UAV Scene

doi:10.3778/j.issn.1673-9418.2005047

Abstract

Abstract:

In order to solve the problem of model drift caused by the scale change of the target vehicle when the traditional correlation filtering algorithm tracks the vehicle in the video taken by the UAV (unmanned aerial vehicle), this paper proposes an improved scale adaptive vehicle tracking algorithm. The algorithm is based on nuclear correlation filtering. By constructing a spatial tracker that distinguishes scales, this paper uses two filters, one to locate the target vehicle??s position, the other to estimate the scale of the target vehicle, in order to quickly determine target-related information and achieve scale adaptation. In addition, in order to solve the problem of poor tracking performance caused by rapid deformation of the target vehicle, color features that are less sensitive to deformation are added to increase the robustness of the filter, and the statistical color feature method is adopted, which is not restricted by template features. The improved algorithm in this paper is tested on 28 vehicle-related video sequences in OTB and UAV data sets. The average distance accuracy is 80.8%, the average success rate is 82.7%, and the FPS reaches 58.24. Experiments show that the algorithm in this paper can improve the detection and tracking effect of vehicles in the drone scene, and can effectively solve the problems caused by the scale change and rapid deformation of the target vehicle. Compared with other nuclear-related filtering algorithms, it has better tracking accuracy and real-time performance.

Key words: unmanned aerial vehicle (UAV) scene, kernel correlation filtering, scale adaptation, statistical color feature

摘要：

为解决传统相关滤波算法对无人机（UAV）拍摄视频中的车辆进行跟踪时，因目标车辆尺度变化而产生模型漂移的问题，提出了一种改进的尺度自适应的车辆跟踪算法。该算法基于核相关滤波，通过构建区分尺度的空间跟踪器，即利用两个滤波器分别对目标车辆的位置进行定位，对目标车辆的尺度进行估计，以此来快速确定目标相关信息，实现对目标车辆尺度的自适应。此外为解决目标车辆因快速形变而导致跟踪效果不佳的问题，还加入了对形变不太敏感的颜色特征，增加滤波器的鲁棒性，采用统计颜色特征方法，不受模板类特征限制。该改进算法在经过OTB和UAV数据集中28段车辆相关的视频序列测试后，平均距离精度为80.8%，平均成功率为82.7%，FPS达到了58.24。实验表明该算法可以提高在无人机场景下对车辆的检测跟踪效果，能够有效解决目标车辆因尺度变化和快速形变产生的问题，相比于其他核相关滤波算法有着更优秀的跟踪精度和实时性。

关键词: 无人机（UAV）场景, 核相关滤波, 尺度自适应, 统计颜色特征

HUANG Jiahui, PENG Li, XIE Linbo. Scale-Adaptive Vehicle Tracking Algorithm in UAV Scene[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(7): 1302-1309.

黄镓辉, 彭力, 谢林柏. 无人机场景下尺度自适应的车辆跟踪算法[J]. 计算机科学与探索, 2021, 15(7): 1302-1309.

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