Contrastive Learning for Multi-Object Tracking with Transformers
Abstract
The DEtection TRansformer (DETR) opened new possibilities for object detection by modeling it as a translation task: converting image features into object-level representations. Previous works typically add expensive modules to DETR to perform Multi-Object Tracking (MOT), resulting in more complicated architectures. We instead show how DETR can be turned into a MOT model by employing an instance-level contrastive loss, a revised sampling strategy and a lightweight assignment method. Our training scheme learns object appearances while preserving detection capabilities and with little overhead. Its performance surpasses the previous state-of-the-art by +2.6 mMOTA on the challenging BDD100K dataset and is comparable to existing transformer-based methods on the MOT17 dataset.
Cite
Text
De Plaen et al. "Contrastive Learning for Multi-Object Tracking with Transformers." Winter Conference on Applications of Computer Vision, 2024.Markdown
[De Plaen et al. "Contrastive Learning for Multi-Object Tracking with Transformers." Winter Conference on Applications of Computer Vision, 2024.](https://mlanthology.org/wacv/2024/plaen2024wacv-contrastive/)BibTeX
@inproceedings{plaen2024wacv-contrastive,
title = {{Contrastive Learning for Multi-Object Tracking with Transformers}},
author = {De Plaen, Pierre-François and Marinello, Nicola and Proesmans, Marc and Tuytelaars, Tinne and Van Gool, Luc},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2024},
pages = {6867-6877},
url = {https://mlanthology.org/wacv/2024/plaen2024wacv-contrastive/}
}