Learning data association for multi-object tracking using only coordinates

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 on the website https://mehdimiah.com/twix . • Our Transformer-based model, TWiX, can learn to associate objects using only coordinates. • We show that motion priors or intersection-over-union measure are not required for tracking. Using pairs of tracks is sufficient. • Tracking with TWiX gives competitive or state-of-the-results on several datasets.