In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach. Yet, relying solely on a single detector is also a major limitation, as useful image information might be ignored. Consequently, this work demonstrates how to fuse two detectors into a tracking system. To obtain the trajectories, we propose to formulate tracking as a weighted graph labeling problem, resulting in a binary quadratic program. As such problems are NP-hard, the solution can only be approximated. Based on the Frank-Wolfe algorithm, we present a new solver that is crucial to handle such difficult problems. Evaluation on pedestrian tracking is provided for multiple scenarios, showing superior results over single detector tracking and standard QP-solvers. Finally, our tracker ranks 2nd on the MOT16 benchmark and 1st on the new MOT17 benchmark, outperforming over 90 trackers.

翻译：为了追踪现场的所有人,跟踪和检测模式已证明是一种非常有效的方法。然而,仅仅依靠单一探测器也是一个重大限制,因为有用的图像信息可能被忽略。因此,这项工作展示了如何将两个探测器连接到一个跟踪系统。为了获得轨迹,我们提议将跟踪设计成一个加权图表标签问题,从而产生一个二进制的四进制程序。由于这类问题是NP硬的,所以只能将解决方案加以近似。根据Frank-Wolfe算法,我们提出了一个新的解决方案,对于处理这类难题至关重要。对行人跟踪进行了多种情景的评价,显示在单一探测器跟踪和标准QP-索尔弗上取得优异的结果。最后,我们的跟踪者在MOT16基准上排名第二,在新的MOT17基准上排名第1,表现超过90个。