Learning Data Association for Multi-Object Tracking using Only
  Coordinates
Mehdi Miah Guillaume-Alexandre Bilodeau Nicolas Saunier
We propose a novel Transformer-based module to address the data association
problem for multi-object tracking. From detections obtained by a pretrained
detector, this module uses only coordinates from bounding boxes to estimate an
affinity score between pairs of tracks extracted from two distinct temporal
windows. This module, named TWiX, is trained on sets of tracks with the
objective of discriminating pairs of tracks coming from the same object from
those which are not. Our module does not use the intersection over union
measure, nor does it requires any motion priors or any camera motion
compensation technique. By inserting TWiX within an online cascade matching
pipeline, our tracker C-TWiX achieves state-of-the-art performance on the
DanceTrack and KITTIMOT datasets, and gets competitive results on the MOT17
dataset. The code will be made available upon publication.