Multi-camera tracking systems are gaining popularity in applications that
demand high-quality tracking results, such as frictionless checkout because
monocular multi-object tracking (MOT) systems often fail in cluttered and
crowded environments due to occlusion. Multiple highly overlapped cameras can
significantly alleviate the problem by recovering partial 3D information.
However, the cost of creating a high-quality multi-camera tracking dataset with
diverse camera settings and backgrounds has limited the dataset scale in this
domain. In this paper, we provide a large-scale densely-labeled multi-camera
tracking dataset in five different environments with the help of an
auto-annotation system. The system uses overlapped and calibrated depth and RGB
cameras to build a high-performance 3D tracker that automatically generates the
3D tracking results. The 3D tracking results are projected to each RGB camera
view using camera parameters to create 2D tracking results. Then, we manually
check and correct the 3D tracking results to ensure the label quality, which is
much cheaper than fully manual annotation. We have conducted extensive
experiments using two real-time multi-camera trackers and a person
re-identification (ReID) model with different settings. This dataset provides a
more reliable benchmark of multi-camera, multi-object tracking systems in
cluttered and crowded environments. Also, our results demonstrate that adapting
the trackers and ReID models on this dataset significantly improves their
performance. Our dataset will be publicly released upon the acceptance of this
work.

本研究提供了一个大规模数据集，利用自动注释系统从不同环境下的高度重叠 RGB 和深度相机生成三维跟踪结果，并通过手动检查和纠正三维跟踪结果来确保标签质量，以提高多相机，多物体跟踪系统的可靠性和性能。