论文标题
利用多相机协会的本地化
Leveraging Localization for Multi-camera Association
论文作者
论文摘要
我们提出了McAssoc，这是一种深入学习方法，用于在多摄像机系统的不同视图中对检测边界框进行关联。绝大多数学术界正在开发单摄像机计算机视觉算法算法，但是，很少有人注意将其纳入多相机系统。在这篇文章中，我们设计了一个3个分支的体系结构，该架构利用了协会和其他交叉定位信息。新的指标，图像对协会精度（IPAA）专为跨摄像机检测关联的性能评估而设计。我们在前面显示的是，本地化信息对于成功的跨摄像机关联至关重要，尤其是在存在相似对象的情况下。本文是在Messytable之前的实验工作，这是一个大规模的基准标记，例如在Mutliple摄像机中的关联。
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.
