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arXiv · 1803.02558

Multi-Channel Pyramid Person Matching Network for Person Re-Identification

Chaojie Mao·Yingming Li·Yaqing Zhang·Zhongfei Zhang·Xi Li
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Abstract

In this work, we present a Multi-Channel deep convolutional Pyramid Person Matching Network (MC-PPMN) based on the combination of the semantic-components and the color-texture distributions to address the problem of person re-identification. In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid person matching network (PPMN) to obtain correspondence representations. These correspondence representations are fused to perform the re-identification task. Further, the proposed framework is optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art literature, especially on the rank-1 recognition rate.

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Chaojie Mao, Yingming Li, Yaqing Zhang, Zhongfei Zhang, Xi Li. 2018-03-07. Multi-Channel Pyramid Person Matching Network for Person Re-Identification. https://arxiv.org/abs/1803.02558

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