The task of multiple people tracking in monocular videos is challenging
because of the numerous difficulties involved: occlusions, varying
environments, crowded scenes, camera parameters and motion. In the
tracking-by-detection paradigm, most approaches adopt person re-identification
techniques based on computing the pairwise similarity between detections.
However, these techniques are less effective in handling long-term occlusions.
By contrast, tracklet (a sequence of detections) re-identification can improve
association accuracy since tracklets offer a richer set of visual appearance
and spatio-temporal cues. In this paper, we propose a tracking framework that
employs a hierarchical clustering mechanism for merging tracklets. To this end,
tracklet re-identification is performed by utilizing a novel multi-stage deep
network that can jointly reason about the visual appearance and spatio-temporal
properties of a pair of tracklets, thereby providing a robust measure of
affinity. Experimental results on the challenging MOT16 and MOT17 benchmarks
show that our method significantly outperforms state-of-the-arts.

本文提出了一个 hierarchical clustering 机制的跟踪框架，以便在利用 multi-stage deep network 进行 tracklet re-identification 的同时，合并 tracklets，从而提高多人追踪准确性。在 MOT16 和 MOT17 基准测试中，实验结果表明，我们的方法显著优于最先进技术。