2015 IEEE International Conference on Computer Vision (ICCV) (2015)
Dec. 7, 2015 to Dec. 13, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.366
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Experimental results on various datasets demonstrate the effectiveness of our approach.
Cameras, Probes, Correlation, Reliability, Pattern matching, Measurement, Image color analysis
Y. Shen, W. Lin, J. Yan, M. Xu, J. Wu and J. Wang, "Person Re-Identification with Correspondence Structure Learning," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 3200-3208.