Multi-object tracking (MOT) on static platforms, such as by surveillance cameras, has achieved significant progress, with various paradigms providing attractive performances. However, the effectiveness of traditional MOT methods is significantly reduced when it comes to dynamic platforms like drones. This decrease is attributed to the distinctive challenges in the MOT-on-drone scenario: (1) objects are generally small in the image plane, blurred, and frequently occluded, making them challenging to detect and recognize; (2) drones move and see objects from different angles, causing the unreliability of the predicted positions and feature embeddings of the objects. This paper proposes DroneMOT, which firstly proposes a Dual-domain Integrated Attention (DIA) module that considers the fast movements of drones to enhance the drone-based object detection and feature embedding for small-sized, blurred, and occluded objects. Then, an innovative Motion-Driven Association (MDA) scheme is introduced, considering the concurrent movements of both the drone and the objects. Within MDA, an Adaptive Feature Synchronization (AFS) technique is presented to update the object features seen from different angles. Additionally, a Dual Motion-based Prediction (DMP) method is employed to forecast the object positions. Finally, both the refined feature embeddings and the predicted positions are integrated to enhance the object association. Comprehensive evaluations on VisDrone2019-MOT and UAVDT datasets show that DroneMOT provides substantial performance improvements over the state-of-the-art in the domain of MOT on drones.

翻译：在静态平台（如监控摄像头）上的多目标跟踪已取得显著进展，多种范式展现出优异的性能。然而，当应用于无人机等动态平台时，传统多目标跟踪方法的效能显著下降。这一下降归因于无人机多目标跟踪场景中的独特挑战：（1）目标在图像平面中通常尺寸小、模糊且频繁被遮挡，使其难以检测与识别；（2）无人机自身运动并从不同视角观测目标，导致目标位置预测与特征嵌入的不可靠性。本文提出DroneMOT方法，首次设计了双域集成注意力模块，该模块考虑无人机的快速运动，以增强针对小尺寸、模糊及遮挡目标的无人机目标检测与特征嵌入能力。随后，引入创新的运动驱动关联方案，该方案同时考虑无人机与目标的协同运动。在运动驱动关联方案中，提出了自适应特征同步技术，以更新从不同视角观测到的目标特征。此外，采用基于双运动的预测方法来预估目标位置。最终，将优化后的特征嵌入与预测位置相结合，以增强目标关联性能。在VisDrone2019-MOT和UAVDT数据集上的综合评估表明，DroneMOT在无人机多目标跟踪领域相比现有最优方法实现了显著的性能提升。