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Person re-identification is an important topic in visual surveillance, which aims at recognizing an individual over disjoint camera views. As a major aspect of person re-identification, distance metric learning has been widely studied to seek a discriminative matching metric. However, most existing distance metric learning methods learn an identical projection matrix for all camera views, while ignoring...
Person re-identification remains a challenging problem due to large variations of poses, occlusions, illumination and camera views. To learn both feature representation and similarity metric simultaneously, deep metric learning methods using triplet convolutional neural network have been applied in person re-identification. In this paper, we propose a body structure based triplet convolutional neural...
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