Multi-view multi-human association and tracking (MvMHAT), is a new but
important problem for multi-person scene video surveillance, aiming to track a
group of people over time in each view, as well as to identify the same person
across different views at the same time, which is different from previous MOT
and multi-camera MOT tasks only considering the over-time human tracking. This
way, the videos for MvMHAT require more complex annotations while containing
more information for self learning. In this work, we tackle this problem with a
self-supervised learning aware end-to-end network. Specifically, we propose to
take advantage of the spatial-temporal self-consistency rationale by
considering three properties of reflexivity, symmetry and transitivity. Besides
the reflexivity property that naturally holds, we design the self-supervised
learning losses based on the properties of symmetry and transitivity, for both
appearance feature learning and assignment matrix optimization, to associate
the multiple humans over time and across views. Furthermore, to promote the
research on MvMHAT, we build two new large-scale benchmarks for the network
training and testing of different algorithms. Extensive experiments on the
proposed benchmarks verify the effectiveness of our method. We have released
the benchmark and code to the public.

多视角多人联合追踪和跟踪（MvMHAT）是用于多人场景视频监视的新而重要的问题，旨在在每个视图中随时间追踪一群人，并在同一时间识别不同视图中的同一人，我们提出了一种自监督学习感知的端到端网络，利用了空间 - 时间自一致性的特性来解决这个问题，并且构建了两个新的大规模数据集来促进该领域的研究。