We present McAssoc, a deep learning approach to the as-sociation of detection bounding boxes in different views ofa multi-camera system. The vast majority of the academiahas been developing single-camera computer vision algo-rithms, however, little research attention has been directedto incorporating them into a multi-camera system. In thispaper, we designed a 3-branch architecture that leveragesdirect association and additional cross localization infor-mation. A new metric, image-pair association accuracy(IPAA) is designed specifically for performance evaluationof cross-camera detection association. We show in the ex-periments that localization information is critical to suc-cessful cross-camera association, especially when similar-looking objects are present. This paper is an experimentalwork prior to MessyTable, which is a large-scale bench-mark for instance association in mutliple cameras.
@article{arxiv.2008.02992,
title = {Leveraging Localization for Multi-camera Association},
author = {Zhongang Cai and Cunjun Yu and Junzhe Zhang and Jiawei Ren and Haiyu Zhao},
journal= {arXiv preprint arXiv:2008.02992},
year = {2020}
}