Can I read Robust and Efficient Graph Correspondence Transfer for Person Re-identification on EtoBox?

Robust and Efficient Graph Correspondence Transfer for Person Re-identification by Zhou, Qin; Fan, Heng; Yang, Hua; Su, Hang; Zheng, Shibao; Wu, Shuang; Ling, Haibin is a scholarly article available to read on EtoBox.

What is Robust and Efficient Graph Correspondence Transfer for Person Re-identification about?

Spatial misalignment caused by variations in poses and viewpoints is one of the most critical issues that hinders the performance improvement in existing person re-identification (Re-ID) algorithms. To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID. Specifically, we propose to establish the patch-wise correspondences of positive training pairs via graph matching. By exploiting both spatial and visual contexts of human appearance in graph matching, meaningful semantic correspondences can be obtained. To circumvent the cumbersome \emph{on-line} graph matching in testing phase, we propose to transfer the \emph{off-line} learned patch-wise correspondences from the positive training pairs to test pairs. In detail, for each test pair, the training pairs with similar pose-pair configurations are selected as references. The matching patterns (i.e., the correspondences) of the selected references are then utilized to calculate the patch-wise feature distances of this test pair. To enhance the robustness of correspondence transfer, we design a novel pose context descriptor to accura

Author

Zhou, Qin; Fan, Heng; Yang, Hua; Su, Hang; Zheng, Shibao; Wu, Shuang; Ling, Haibin

Published

2018

Language

EN