论文标题
按计数跟踪：在人群密度图上使用网络流进行跟踪多个目标
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
论文作者
论文摘要
最先进的多对象跟踪〜（MOT）方法遵循逐个检测范式的跟踪，其中通过关联对象检测器的人均输出来获得对象轨迹。但是，在拥挤的场景中，由于沉重的阻塞和高人群密度，探测器通常无法获得准确的检测。在本文中，我们提出了一个新的MOT范式，该范式是按规范进行跟踪，该范式是为拥挤的场景量身定制的。使用人群密度图，我们将多个目标作为网络流程程序共同模型检测，计数和跟踪，同时在整个视频中找到了多个目标的全局最佳检测和轨迹。这与先前的MOT方法相反，后者忽略了人群密度，因此很容易出现拥挤的场景中的错误，或者使用启发式密度感知的点轨迹依靠次优的两步过程来匹配目标。
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded scenes, however, detectors often fail to obtain accurate detections due to heavy occlusions and high crowd density. In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and trajectories of multiple targets over the whole video. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to errors in crowded scenes, or rely on a suboptimal two-step process using heuristic density-aware point-tracks for matching targets.Our approach yields promising results on public benchmarks of various domains including people tracking, cell tracking, and fish tracking.
