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arXiv · 1702.06294

Learning Compact Appearance Representation for Video-based Person Re-Identification

Wei Zhang·Shengnan Hu·Kan Liu·Zhengjun Zha
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Abstract

This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs). Unlike previous work, we intend to extract a compact yet discriminative appearance representation from several frames rather than the whole sequence. Specifically, given a video, the representative frames are selected based on the walking profile of consecutive frames. A multiple CNN architecture incorporated with feature pooling is proposed to learn and compile the features of the selected representative frames into a compact description about the pedestrian for identification. Experiments are conducted on benchmark datasets to demonstrate the superiority of the proposed method over existing person re-identification approaches.

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Wei Zhang, Shengnan Hu, Kan Liu, Zhengjun Zha. 2017-02-21. Learning Compact Appearance Representation for Video-based Person Re-Identification. https://arxiv.org/abs/1702.06294

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