  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.
