Configure T, max distance which keeps association about py-motmetrics HOT 3 CLOSED

Let me clarify. In py-motmetrics the threshold you mention to keep associations alive is not an explicit parameter. Instead, it is part of the given distance matrix. All elements that cannot pair are signalled with NAN.

What py-motmetrics does, it allows you to compute distances between hypothesis of your tracker and ground truths of the current frame. It then prevents potential pairings (of established or new tracks) that exceed a given threshold via indication with NAN in the input distance matrix.

Why not a making a parameter? Depending on the application at hand you might want to apply different thresholds to different objects based on some higher level logic (think for example: your tracker outputs a confidence for each hypothesis). This is unattainable with a global parameter, but easily achieved by appling different NAN rules to different objects/hypothesis.

So, [0] 2.1.1 states

First of all, the correspondence between an object oi and
a hypothesis hj should not be made if their distance disti,j
exceeds a certain threshold T.

This statement does not relate to tracks already established.

In [2] it seems like a greedy assignment algorithm is used on the principle of first-come-first-serve. In contrast py-motmetrics assigns the remaining possible pairs to matches, misses and switches using a per-frame optimal algorithm based on minimum costs (LAP). Where remaining refers to all potential pairs, after already established ones have been continued.

Re-establish:
https://github.com/cheind/py-motmetrics/blob/master/motmetrics/mot.py#L171

Assign remainings:
https://github.com/cheind/py-motmetrics/blob/master/motmetrics/mot.py#L190

Does this clear up things for you?

from py-motmetrics.

Mmm I see, so it is something which has to be done before we fill in the distance matrix. Also, I realized I completely misunderstood 2.1.1 in the paper, sorry about that :D

from py-motmetrics.

Correct. You are welcome, I had to look it up too! ;)

from py-motmetrics.