The main challenge of Multi-Object Tracking~(MOT) lies in maintaining a
continuous trajectory for each target. Existing methods often learn reliable
motion patterns to match the same target between adjacent frames and
discriminative appearance features to re-identify the lost targets after a long
period. However, the reliability of motion prediction and the discriminability
of appearances can be easily hurt by dense crowds and extreme occlusions in the
tracking process. In this paper, we propose a simple yet effective multi-object
tracker, i.e., MotionTrack, which learns robust short-term and long-term
motions in a unified framework to associate trajectories from a short to long
range. For dense crowds, we design a novel Interaction Module to learn
interaction-aware motions from short-term trajectories, which can estimate the
complex movement of each target. For extreme occlusions, we build a novel
Refind Module to learn reliable long-term motions from the target's history
trajectory, which can link the interrupted trajectory with its corresponding
detection. Our Interaction Module and Refind Module are embedded in the
well-known tracking-by-detection paradigm, which can work in tandem to maintain
superior performance. Extensive experimental results on MOT17 and MOT20
datasets demonstrate the superiority of our approach in challenging scenarios,
and it achieves state-of-the-art performances at various MOT metrics.

本文提出了一种名为 MotionTrack 的简单而有效的多目标跟踪器，它在统一框架中学习健壮的短期和长期运动，以将轨迹从短范围关联到长范围。通过在检测范例跟踪中嵌入 Interaction Module 和 Refind Module​​，在挑战性场景中，该方法在各种 MOT 指标上均实现了最先进的性能。

MotionTrack: 学习多目标跟踪的强健短期和长期运动

MotionTrack: Learning Robust Short-term and Long-term Motions for  Multi-Object Tracking

Despite the recent advances in multiple object tracking (MOT), achieved by
joint detection and tracking, dealing with long occlusions remains a challenge.
This is due to the fact that such techniques tend to ignore the long-term
motion information. In this paper, we introduce a probabilistic autoregressive
motion model to score tracklet proposals by directly measuring their
likelihood. This is achieved by training our model to learn the underlying
distribution of natural tracklets. As such, our model allows us not only to
assign new detections to existing tracklets, but also to inpaint a tracklet
when an object has been lost for a long time, e.g., due to occlusion, by
sampling tracklets so as to fill the gap caused by misdetections. Our
experiments demonstrate the superiority of our approach at tracking objects in
challenging sequences; it outperforms the state of the art in most standard MOT
metrics on multiple MOT benchmark datasets, including MOT16, MOT17, and MOT20.

该研究提出了一种基于自回归运动模型的多目标跟踪算法，能够处理长期的遮挡问题，通过训练模型去学习自然轨迹的分布，与传统方法相比，在多个 MOT 基准数据集上表现更好。