Abstract: In recent years, UAVs have been widely used in the field of vehicle tracking due to their small size and flexibility. However, when UAVs fly at high altitude, there are few pixel points, crowding, and occlusion of vehicle objects in their captured images. Moreover, existing multi-object tracking research methods use Kalman filter prediction when nonlinear motion occurs during vehicle occlusion, and the problem of inaccurate vehicle position prediction occurs. In order to solve these problems, this paper adopts tracking by detection (TBD) paradigm, which firstly improves the YOLOv8 detection algorithm by introducing BiFormer sparse dynamic attention module in the network structure for extracting small object feature information. Meanwhile, the lightweight upsampling operator CARAFE is used to replace the original nearest-neighbor interpolation upsampling, which reduces the problem of small-object feature loss in the upsampling process. Then a lightweight tracking model FA-SORT is proposed, and three improvements are proposed for the SORT algorithm：improving KF, adding speed-direction consistency matching and detection value matching. Finally, the improved YOLOv8 algorithm is validated on a homemade combination of several vehicle datasets. The experimental results show that the precision is improved by 0.97% and the recall is improved by 0.898% compared with YOLOv8. The proposed FA-SORT algorithm is validated using the UAVDT dataset, and the results show that the first HOTA metric reaches 70.05%, IDF1 reaches 87.45%, and the tracking speed reaches 29.93 FPS compared to existing multi-objective tracking algorithms. The superiority of the FA-SORT tracking algorithm for multi-vehicle tracking tasks is verified.

Key words: multi-vehicle tracking, object detection, velocity-direction consistency matching, detection value matching, UAV images

摘要： 近年来，无人机因体积小、灵活性好等优势被广泛应用在车辆跟踪领域。当无人机在高空飞行时，其捕捉的图像中车辆目标存在像素点少、拥挤以及被遮挡的情况。现有的多目标跟踪研究方法在车辆被遮挡过程中发生非线性运动时，使用卡尔曼滤波预测，会出现车辆位置预测不准确的问题。为了解决这些问题，采用先检测后跟踪（tracking by detection，TBD）范式，对YOLOv8检测算法进行改进，在网络结构中引入了BiFormer稀疏动态注意力模块，用于提取小目标特征信息。同时使用轻量级上采样算子CARAFE替换原最近邻插值上采样，减少上采样过程中小目标特征丢失的问题。提出一种轻量化跟踪模型FA-SORT，针对SORT算法提出三点改进：改进KF、添加速度方向一致性匹配和检测值匹配。在自制地组合了多个车辆数据集上验证改进的YOLOv8算法。实验结果表明，与YOLOv8相比，精确率（precision）提高了0.97%，召回率（recall）提高了0.898%。对所提出的FA-SORT算法使用UAVDT数据集进行验证，结果表明，与现有的多目标跟踪算法相比，HOTA指标首个达到70.05%，IDF1达到87.45%，跟踪速度达到29.93?FPS。验证了FA-SORT跟踪算法在多车辆跟踪任务中的优越性。

关键词: 多车辆跟踪, 目标检测, 速度方向一致性匹配, 检测值匹配, 无人机图像