  Multi-object tracking (MOT) is a challenging vision task that aims to detect
individual objects within a single frame and associate them across multiple
frames. Recent MOT approaches can be categorized into two-stage
tracking-by-detection (TBD) methods and one-stage joint detection and tracking
(JDT) methods. Despite the success of these approaches, they also suffer from
common problems, such as harmful global or local inconsistency, poor trade-off
between robustness and model complexity, and lack of flexibility in different
scenes within the same video. In this paper we propose a simple but robust
framework that formulates object detection and association jointly as a
consistent denoising diffusion process from paired noise boxes to paired
ground-truth boxes. This novel progressive denoising diffusion strategy
substantially augments the tracker's effectiveness, enabling it to discriminate
between various objects. During the training stage, paired object boxes diffuse
from paired ground-truth boxes to random distribution, and the model learns
detection and tracking simultaneously by reversing this noising process. In
inference, the model refines a set of paired randomly generated boxes to the
detection and tracking results in a flexible one-step or multi-step denoising
diffusion process. Extensive experiments on three widely used MOT benchmarks,
including MOT17, MOT20, and Dancetrack, demonstrate that our approach achieves
competitive performance compared to the current state-of-the-art methods.
