Multiple object tracking (MOT) has been successfully investigated in computer
vision.
However, MOT for the videos captured by unmanned aerial vehicles (UAV) is
still challenging due to small object size, blurred object appearance, and very
large and/or irregular motion in both ground objects and UAV platforms.
In this paper, we propose FOLT to mitigate these problems and reach fast and
accurate MOT in UAV view.
Aiming at speed-accuracy trade-off, FOLT adopts a modern detector and
light-weight optical flow extractor to extract object detection features and
motion features at a minimum cost.
Given the extracted flow, the flow-guided feature augmentation is designed to
augment the object detection feature based on its optical flow, which improves
the detection of small objects.
Then the flow-guided motion prediction is also proposed to predict the
object's position in the next frame, which improves the tracking performance of
objects with very large displacements between adjacent frames.
Finally, the tracker matches the detected objects and predicted objects using
a spatially matching scheme to generate tracks for every object.
Experiments on Visdrone and UAVDT datasets show that our proposed model can
successfully track small objects with large and irregular motion and outperform
existing state-of-the-art methods in UAV-MOT tasks.

通过提出 FOLT 方法，本文致力于解决无人机视角下的多目标跟踪问题，通过采用现代化检测器和轻量级光流提取器，提取目标检测特征和运动特征，并利用光流增强目标检测特征，预测对象在下一帧中的位置，并通过空间匹配方案匹配检测到的对象和预测的对象，从而成功地跟踪小型、大规模且具有不规则运动的对象，在无人机多目标跟踪任务中胜过现有的最先进方法。