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.

本文提出了一种新的基于人群密度图的多目标追踪（MOT）算法 —— 计数式追踪。通过联合建模检测、计数和跟踪多个目标为网络流算法，该算法同时找到全局最优的多个目标检测和轨迹。该方法在人员、细胞和鱼群跟踪等各领域的公共基准测试中表现出了良好的结果。