Although there is a significant development in 3D Multi-view Multi-person
Tracking (3D MM-Tracking), current 3D MM-Tracking frameworks are designed
separately for footprint and pose tracking. Specifically, frameworks designed
for footprint tracking cannot be utilized in 3D pose tracking, because they
directly obtain 3D positions on the ground plane with a homography projection,
which is inapplicable to 3D poses above the ground. In contrast, frameworks
designed for pose tracking generally isolate multi-view and multi-frame
associations and may not be robust to footprint tracking, since footprint
tracking utilizes fewer key points than pose tracking, which weakens multi-view
association cues in a single frame. This study presents a Unified Multi-view
Multi-person Tracking framework to bridge the gap between footprint tracking
and pose tracking. Without additional modifications, the framework can adopt
monocular 2D bounding boxes and 2D poses as the input to produce robust 3D
trajectories for multiple persons. Importantly, multi-frame and multi-view
information are jointly employed to improve the performance of association and
triangulation. The effectiveness of our framework is verified by accomplishing
state-of-the-art performance on the Campus and Shelf datasets for 3D pose
tracking, and by comparable results on the WILDTRACK and MMPTRACK datasets for
3D footprint tracking.

本研究提出了一个统一的多视角多人追踪框架，以弥合足迹追踪和姿态追踪之间的差距。框架可采用单眼 2D 边界框和 2D 姿态作为输入，无需附加修改即可为多人生成强健的 3D 轨迹，并通过联合使用多帧和多视角信息来提高关联和三角剖分的性能。