Multi-Camera Multi-Object Tracking (MC-MOT) utilizes information from
multiple views to better handle problems with occlusion and crowded scenes.
Recently, the use of graph-based approaches to solve tracking problems has
become very popular. However, many current graph-based methods do not
effectively utilize information regarding spatial and temporal consistency.
Instead, they rely on single-camera trackers as input, which are prone to
fragmentation and ID switch errors. In this paper, we propose a novel
reconfigurable graph model that first associates all detected objects across
cameras spatially before reconfiguring it into a temporal graph for Temporal
Association. This two-stage association approach enables us to extract robust
spatial and temporal-aware features and address the problem with fragmented
tracklets. Furthermore, our model is designed for online tracking, making it
suitable for real-world applications. Experimental results show that the
proposed graph model is able to extract more discriminating features for object
tracking, and our model achieves state-of-the-art performance on several public
datasets.

提出了一种新颖的可重配置图模型，通过首先在空间上关联所有检测到的对象，然后将其重新配置为时间图来进行时间关联，从而能够提取出稳健的空间和时间感知特征，并解决跟踪过程中出现的分段轨迹问题。该模型适用于在线跟踪，并在多个公共数据集上取得了最先进的性能。

ReST: 可重构的时空图模型用于多摄像头多目标跟踪

ReST: A Reconfigurable Spatial-Temporal Graph Model for Multi-Camera  Multi-Object Tracking

Multi-Camera Multi-Object Tracking is currently drawing attention in the
computer vision field due to its superior performance in real-world
applications such as video surveillance in crowded scenes or in wide spaces. In
this work, we propose a mathematically elegant multi-camera multiple object
tracking approach based on a spatial-temporal lifted multicut formulation. Our
model utilizes state-of-the-art tracklets produced by single-camera trackers as
proposals. As these tracklets may contain ID-Switch errors, we refine them
through a novel pre-clustering obtained from 3D geometry projections. As a
result, we derive a better tracking graph without ID switches and more precise
affinity costs for the data association phase. Tracklets are then matched to
multi-camera trajectories by solving a global lifted multicut formulation that
incorporates short and long-range temporal interactions on tracklets located in
the same camera as well as inter-camera ones. Experimental results on the
WildTrack dataset yield near-perfect performance, outperforming
state-of-the-art trackers on Campus while being on par on the PETS-09 dataset.

本文提出了一种基于空时提升 Multicut 公式的多摄像机多目标跟踪方法，其利用单摄像机跟踪器生成的先进 tracklet 作为提议，并通过 3D 几何投影获得新颖的预聚类以优化 tracklet，最后通过一个全局提升 Multicut 公式将 tracklet 匹配到多摄像机轨迹，并在 WildTrack 数据集上获得了近乎完美的性能。