Online multi-object tracking (MOT) is a fundamental problem in video analysis and multimedia applications. The major challenge in the popular tracking-by-detection framework is knowing how to associate candidate detections results with existing tracklets. In this, we propose a non-local attention association approach and apply it to a unified online MOT framework that integrates the merits of single object tracking and data association methods. Specifically, we use non-local attention association networks (NAAN) to incorporate both spatial and temporal characteristics to associate new detections. The non-local attention mechanism generates global attention maps across space and time, enabling the network to focus on the whole tracklet information, as opposed to the local attention mechanism to overcome the problems of noisy detections, occlusion, and frequent interactions between targets. Experimental results on MOT benchmark datasets show that the proposed algorithm performs favorably against various online trackers on the basis of identity-preserving metrics.

在线多目标跟踪（MOT）是视频分析和多媒体应用程序中的一个基本问题。流行的按检测跟踪框架中的主要挑战是知道如何将候选检测结果与现有的tracklet相关联。在本文中，我们提出了一种非本地注意力关联方法，并将其应用于统一的在线MOT框架，该框架集成了单个对象跟踪和数据关联方法的优点。具体来说，我们使用非本地注意力关联网络（NAAN）来结合时空特征来关联新的检测。非本地注意力机制会生成跨时空的全局注意力图，从而使网络能够专注于整个小径信息，而与本地注意力机制相反，该机制克服了嘈杂的检测，遮挡，目标之间的频繁互动。在MOT基准数据集上的实验结果表明，该算法在保持身份的指标基础上，对各种在线跟踪器具有良好的性能。