In Multiple Object Tracking (MOT), tracking-by-detection methods have stood
the test for a long time, which split the process into two parts according to
the definition: object detection and association. They leverage robust
single-frame detectors and treat object association as a post-processing step
through hand-crafted heuristic algorithms and surrogate tasks. However, the
nature of heuristic techniques prevents end-to-end exploitation of training
data, leading to increasingly cumbersome and challenging manual modification
while facing complicated or novel scenarios. In this paper, we regard this
object association task as an End-to-End in-context ID prediction problem and
propose a streamlined baseline called MOTIP. Specifically, we form the target
embeddings into historical trajectory information while considering the
corresponding IDs as in-context prompts, then directly predict the ID labels
for the objects in the current frame. Thanks to this end-to-end process, MOTIP
can learn tracking capabilities straight from training data, freeing itself
from burdensome hand-crafted algorithms. Without bells and whistles, our method
achieves impressive state-of-the-art performance in complex scenarios like
DanceTrack and SportsMOT, and it performs competitively with other
transformer-based methods on MOT17. We believe that MOTIP demonstrates
remarkable potential and can serve as a starting point for future research. The
code is available at this https URL

在多目标跟踪中，我们将目标关联任务视为一种端到端的上下文 ID 预测问题，并提出了一种简化版的 MOTIP 方法，通过直接预测当前帧中物体的 ID 标签来学习跟踪能力，从而避免了繁琐的手工算法。我们的方法在复杂场景中表现出色，并与其他基于 Transformer 的方法在 MOT17 上竞争力十足，展示出了显著的潜力，可作为未来研究的起点。

多目标追踪作为 ID 预测

Multiple Object Tracking as ID Prediction

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.

提出了一种简单但健壮的框架，将物体检测和关联作为从配对噪声框到配对真实框的一致去噪扩散过程来联合建模，通过新颖的渐进去噪扩散策略大幅提升了跟踪器的效果，在推理中，模型通过灵活的一步或多步去噪扩散过程，将一组随机生成的配对框优化为检测和跟踪结果，大范围实验证明了该方法在多个 MOT 基准测试上与当前最先进的方法相当。