This paper aims to address critical issues in the field of Multi-Object
Tracking (MOT) by proposing an efficient and computationally resource-efficient
end-to-end multi-object tracking model, named MO-YOLO. Traditional MOT methods
typically involve two separate steps: object detection and object tracking,
leading to computational complexity and error propagation issues. Recent
research has demonstrated outstanding performance in end-to-end MOT models
based on Transformer architectures, but they require substantial hardware
support. MO-YOLO combines the strengths of YOLO and RT-DETR models to construct
a high-efficiency, lightweight, and resource-efficient end-to-end multi-object
tracking network, offering new opportunities in the multi-object tracking
domain. On the MOT17 dataset, MOTR\cite{zeng2022motr} requires training with 8
GeForce 2080 Ti GPUs for 4 days to achieve satisfactory results, while MO-YOLO
only requires 1 GeForce 2080 Ti GPU and 12 hours of training to achieve
comparable performance.

该论文提出了一种高效、资源利用率高的端到端多目标跟踪模型 MO-YOLO，通过结合 YOLO 和 RT-DETR 模型的优势，构建了一种高效、轻量级的多目标跟踪网络，为多目标跟踪领域提供了新的机会。