Robust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

鲁棒且高性能的视觉多目标跟踪是计算机视觉中的一大挑战，尤其是在无人机场景中。本文提出了无人机系统中的在线多目标跟踪（MOT）方法来处理小目标检测和类不平衡挑战，并将深度高分辨率表示网络和数据关联方法的优点整合在一个统一的框架中。具体来说，在将逐条检测架构应用于我们的跟踪框架的同时，提出了一种分层深度高分辨率网络（HDHNet），该网络鼓励模型处理不同类型和规模的目标，并在在线过程中提取更有效，更全面的功能学习。之后，将提取的特征馈入不同的预测网络以进行有趣的目标识别。除了，结合焦距损耗和GIoU损耗，提出了一种可调节的融合损耗函数，以解决类不平衡和硬样本的问题。在跟踪过程中，将这些检测结果应用于每个帧中经过改进的DeepSORT MOT算法，该算法可充分利用目标外观特征在实际基础上进行逐一匹配。VisDrone2019 MOT基准测试结果表明，与最新方法相比，拟议的无人机MOT系统具有最高的准确性和最佳的鲁棒性。可以充分利用目标外观功能在实际基础上进行一对一匹配。在VisDrone2019 MOT基准测试中的实验结果表明，与最新方法相比，所提出的UAV MOT系统具有最高的准确性和最佳的鲁棒性。可以充分利用目标外观功能在实际基础上进行一对一匹配。在VisDrone2019 MOT基准测试中的实验结果表明，与最新方法相比，所提出的UAV MOT系统具有最高的准确性和最佳的鲁棒性。