  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.
