In recent years,object detection and data association have getting remarkable progress which are the core components for multi-object tracking. In multi-object tracking field,the main strategy is tracking-by-detection. Although the detection based tracking method can get great results, it is relies on the performance of the detector. In complex scene, detector can not provide reliable results. Moreover,due to the incorrect detection results, data association process can not be trusted. Based on this motivation, this paper focuses on improving the accuracy of detection and data association. We introduce the efficient channel attention module to the backbone network, which can adaptively extract important information in images. Furthermore, we apply switchable atrous convolution in the network to dynamically adjust the receptive field according to object changes. In data association process, the appearance features with minimum occlusion are saved for each existing trajectory, which are used for re-associate after the objects are lost. Extensive experiments on MOT16,MOT17 and MOT20 challenging datasets demonstrate that our method is comparable with the state-of-the-art multi-object tracking methods.

近年来，作为多目标跟踪的核心组成部分的目标检测和数据关联取得了长足的进步。在多目标跟踪领域，主要策略是逐条跟踪。尽管基于检测的跟踪方法可以获得很好的结果，但是它依赖于检测器的性能。在复杂的场景中，探测器无法提供可靠的结果。此外，由于检测结果不正确，因此不能信任数据关联过程。基于这种动机，本文着重于提高检测和数据关联的准确性。我们将有效的频道关注模块引入骨干网络，该模块可以自适应地提取图像中的重要信息。此外，我们在网络中应用可切换的无规卷积，以根据对象的变化动态调整接收场。在数据关联过程中，将为每个现有轨迹保存具有最小遮挡的外观特征，这些特征将在对象丢失后用于重新关联。在具有挑战性的MOT16，MOT17和MOT20数据集上进行的大量实验表明，我们的方法可与最新的多对象跟踪方法相媲美。