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Person re-identification (PRID) is a challenging problem in multi-camera surveillance systems. In this paper, we propose a novel Datum-Adaptive Local Metric learning method for PRID, which learns individual local feature projection for each image sample according to the current data distribution and projects all samples into a common discriminative space for similarity measure. We adopt an approximate...
Person re-identification is a challenging problem in multi-camera surveillance systems. Most current methods always aim at learning a global distance metric to overcome the visual appearance changes between images from different cameras. However, the feature variations between images are not constant over the entire feature space, thus one global metric is not always applicable to all feature variation...
Person re-identification is a challenging problem in computer vision due to large variations of appearance among different cameras. Recently, metric learning is widely used to model the transformation between cameras. However, traditional metric learning based methods only learn one metric for the whole feature space, which cannot model different kinds of appearance variations well. In this paper,...
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