行人再识别。人再次鉴定的准确性可以显著提高给定一个训练集,演示了外表的变化与非重叠的两个摄像头。我们测试时是否能保持这种优势直接标注的训练集并非对所有现场camera-pairs可用。给定的训练集捕捉相机A和B之间的对应关系和不同的训练集捕捉相机B和C之间的对应关系,传递鉴定算法(TRID)建议提供了一个分类器(A,C)对外观。该方法是基于统计建模和使用一个边缘化的推理过程。这种方法可以显著减少注释工作固有的学习系统。-Person re-identification accuracy can be significantly improved given a training set that demonstrates changes in appearances associated with the two non-overlapping cameras involved. Here we test whether this advantage can be maintained when directly annotated training sets are not available for all camera-pairs at the site. Given the training sets capturing correspondences between cameras A and B and a different training set capturing correspondences between cameras B and C, the Transitive Re-IDentification algorithm (TRID) suggested here provides a classifier for (A,C) appearance pairs. The proposed method is based on statistical modeling and uses a marginalization process for the inference. This approach significantly reduces the annotation effort inherent in a learning system, which goes down　O(N^2) to O(N), for a site containing N cameras. Moreover, when adding camera (N+1), only one inter-camera training set is required for establishing all correspondences. In our experiments w