Human pose estimation and tracking are fundamental tasks for understanding
human behaviors in videos. Existing top-down framework-based methods usually
perform three-stage tasks: human detection, pose estimation and tracking.
Although promising results have been achieved, these methods rely heavily on
high-performance detectors and may fail to track persons who are occluded or
miss-detected. To overcome these problems, in this paper, we develop a novel
keypoint confidence network and a tracking pipeline to improve human detection
and pose estimation in top-down approaches. Specifically, the keypoint
confidence network is designed to determine whether each keypoint is occluded,
and it is incorporated into the pose estimation module. In the tracking
pipeline, we propose the Bbox-revision module to reduce missing detection and
the ID-retrieve module to correct lost trajectories, improving the performance
of the detection stage. Experimental results show that our approach is
universal in human detection and pose estimation, achieving state-of-the-art
performance on both PoseTrack 2017 and 2018 datasets.

本研究开发了一种新型关键点置信网络和跟踪流程，以改善自上而下方法中的人体检测和姿势估计，实验结果显示我们的方法在人体检测和姿势估计方面具有普适性，并在 PoseTrack 2017 和 2018 数据集上取得最先进的性能。

借助置信度网络优化多人姿态跟踪

Improving Multi-Person Pose Tracking with A Confidence Network

This paper presents a study of two tracking algorithms (SORT~\cite{7533003}
and Tracktor++~\cite{2019}) that were ranked first positions on the MOT
Challenge leaderboard (The MOTChallenge web page: this https URL ).
The purpose of this study is to discover the techniques used and to provide
useful insights about these algorithms in the tracking pipeline that could
improve the performance of MOT tracking algorithms. To this end, we adopted the
popular tracking-by-detection approach. We trained our own Pedestrian Detection
model using the MOT17Det dataset (MOT17Det :
this https URL ). We also used a re-identification
model trained on MOT17 dataset (MOT17 : this https URL )
for Tracktor++ to reduce the false re-identification alarms. We then present
experimental results which shows that Tracktor++ is a better multi-person
tracking algorithm than SORT. We also performed ablation studies to discover
the contribution of re-identification(RE-ID) network and motion to the results
of Tracktor++. We finally conclude by providing some recommendations for future
research.

这篇论文研究了两种在 MOT 挑战排行榜上名列前茅的追踪算法（SORT 和 Tracktor++），目的在于发现这些算法在追踪流程中采用的技术，并提供有价值的见解以改进 MOT 追踪算法的性能。研究采用了常用的检测跟踪方法，并使用自己训练的行人检测模型和跟踪模型，经实验结果表明 Tracktor++ 是比 SORT 更好的多人追踪算法。此外，进行了消融研究以发现 Tracktor++ 结果中重识别网络和运动的贡献，并提供了一些建议供未来研究参考。