实时多目标跟踪算法取得了理想的跟踪性能，但大多数现有算法的跟踪速度较慢，且随着背景复杂度的增加，跟踪精度也随之降低。针对此问题，本文提出了基于在线数据关联的行人实时跟踪算法。首先，设计了核相关滤波和卡尔曼滤波双轨道预测机制，配合DeepSORT中的级联匹配形成了预测-跟踪-校准体系，使数据关联更加可靠。此外，在目标检测部分引入了注意力机制，通过强化目标对象的位置信息增强特征表示能力，从而提升跟踪的精度。该模型在MOT16数据集上的MOTA达到了66.5%、IDF1达到了64.2%、IDSW达到了641。与DeepSORT算法对比，MOTA和IDF1分别提升了13%和13.2%，IDSW下降了410。本文算法有助于解决行人实时跟踪时出现的目标误检、漏检等问题，在跟踪中对严重遮挡情况仍保持了较高的跟踪精度，在复杂环境下可以实现行人实时稳定跟踪。

Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos has attracted attention. Because of the observation perspectives of UAV, the object scale changes dramatically and is relatively small. Besides, most MOT algorithms in UAV videos cannot achieve real-time due to the tracking-by-detection paradigm. We propose a feature-aligned attention network (FAANet). It mainly consists of a channel and spatial attention module and a feature-aligned aggregation module. We also improve the real-time performance using the joint-detection-embedding paradigm and structural re-parameterization technique. We validate the effectiveness with extensive experiments on UAV detection and tracking benchmark, achieving new state-of-the-art 44.0 MOTA, 64.6 IDF1 with 38.24 frames per second running speed on a single 1080Ti graphics processing unit.