Multi-object tracking in videos requires to solve a fundamental problem of
one-to-one assignment between objects in adjacent frames. Most methods address
the problem by first discarding impossible pairs whose feature distances are
larger than a threshold, followed by linking objects using Hungarian algorithm
to minimize the overall distance. However, we find that the distribution of the
distances computed from Re-ID features may vary significantly for different
videos. So there isn't a single optimal threshold which allows us to safely
discard impossible pairs. To address the problem, we present an efficient
approach to compute a marginal probability for each pair of objects in real
time. The marginal probability can be regarded as a normalized distance which
is significantly more stable than the original feature distance. As a result,
we can use a single threshold for all videos. The approach is general and can
be applied to the existing trackers to obtain about one point improvement in
terms of IDF1 metric. It achieves competitive results on MOT17 and MOT20
benchmarks. In addition, the computed probability is more interpretable which
facilitates subsequent post-processing operations.

本文提出了一种基于边缘概率计算的目标多目标追踪方法，可以取代原始特征距离，解决了不同视频中距离分布变化引起的单一特征距离阈值不准确的问题，并在 MOT17 和 MOT20 基准测试中获得了较好的结果，并且计算得到的概率更易于解释，有利于后续的后处理操作。