This paper addresses the problem of multi-object tracking in Unmanned Aerial Vehicle (UAV) footage. It plays a critical role in various UAV applications, including traffic monitoring systems and real-time suspect tracking by the police. However, this task is highly challenging due to the fast motion of UAVs, as well as the small size of target objects in the videos caused by the high-altitude and wide angle views of drones. In this study, we thus introduce a simple yet more effective method compared to previous work to overcome these challenges. Our approach involves a new tracking strategy, which initiates the tracking of target objects from low-confidence detections commonly encountered in UAV application scenarios. Additionally, we propose revisiting traditional appearance-based matching algorithms to improve the association of low-confidence detections. To evaluate the effectiveness of our method, we conducted benchmark evaluations on two UAV-specific datasets (VisDrone2019, UAVDT) and one general object tracking dataset (MOT17). The results demonstrate that our approach surpasses current state-of-the art methodologies, highlighting its robustness and adaptability in diverse tracking environments. Furthermore, we have improved the annotation of the UAVDT dataset by rectifying several errors and addressing omissions found in the original annotations. We will provide this refined version of the dataset to facilitate better benchmarking in the field.

本研究解决了无人机录像中的多目标跟踪问题，这是交通监控和实时嫌疑人追踪等无人机应用中的关键挑战。我们提出了一种简单而有效的新跟踪策略，着重于从低置信度检测开始跟踪目标，结合传统外观匹配算法改进低置信度检测的关联。实验结果表明，我们的方法在多种跟踪环境中展现出优越性能，并改进了UAVDT数据集的注释，促进了更好的基准测试。

SFTrack：一种稳健的尺度和运动自适应算法，用于跟踪小型快速移动物体