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Similarity Learning with Spatial Constraints for Person Re-identification
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Pose variation remains one of the major factors that adversely affect the accuracy of person re-identification. Such variation is not arbitrary as body parts (e.g. head, torso, legs) have relative stable spatial distribution. Breaking down the variability of global appearance regarding the spatial distribution potentially benefits the person matching. We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion.
doi:10.1109/cvpr.2016.142
dblp:conf/cvpr/ChenYCZ16
fatcat:wqtelwwo7bfzhjla4o6ttzx5cy