MO-YOLO: End-to-End Multiple-Object Tracking Method with YOLO and Decoder

arXiv:2310.17170v3 Announce Type: replace
Abstract: In the field of multi-object tracking (MOT), recent Transformer based end-to-end models like MOTR have demonstrated exceptional performance on datasets such as DanceTracker. However, the computational demands of these models present challenges in training and deployment. Drawing inspiration from successful models like GPT, we present MO-YOLO, an efficient and computationally frugal end-to-end MOT model. MO-YOLO integrates principles from You Only Look Once (YOLO) and RT-DETR, adopting a decoder-only approach. By leveraging the decoder from RT-DETR …

abstract arxiv challenges computational cs.cv datasets decoder deployment gpt however inspiration multiple object performance tracking training transformer type yolo