Abstract In this paper, we propose a new method for online multiple people tracking, which combines the detection process and the single object tracking process, and establishes the interactions between them. The detector detects objects in the still images which ignores the sequential information. Meantime, the single object tracker does not use the category semantic information during tracking. To take both the sequential and semantic information into account, we exchange information among the detector and the trackers. More specifically, the trackers deliver sequential information to the detector by providing the detector with the extra proposals. The detector supplements each tracker with the robust semantic information by using bounding box regression to modify the tracking result. Besides, the interactions also happen among the trackers through the occlusion speculation, the perspective model interpretation and the trajectory merging process. The experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art MOT methods.

中文翻译：

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基于语义和场景信息的联合检测和多人跟踪

摘要 在本文中，我们提出了一种在线多人跟踪的新方法，它结合了检测过程和单个对象跟踪过程，并建立了它们之间的相互作用。检测器检测静止图像中的物体，忽略顺序信息。同时，单个对象跟踪器在跟踪过程中不使用类别语义信息。为了同时考虑顺序和语义信息，我们在检测器和跟踪器之间交换信息。更具体地说，跟踪器通过向检测器提供额外的建议来将顺序信息传递给检测器。检测器通过使用边界框回归来修改跟踪结果，为每个跟踪器补充鲁棒的语义信息。除了，通过遮挡推测、透视模型解释和轨迹合并过程，跟踪器之间也会发生相互作用。实验结果表明，所提出的算法与最先进的 MOT 方法相比具有良好的性能。