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.
@article{arxiv.1702.06294,
title = {Learning Compact Appearance Representation for Video-based Person Re-Identification},
author = {Wei Zhang and Shengnan Hu and Kan Liu and Zhengjun Zha},
journal= {arXiv preprint arXiv:1702.06294},
year = {2019}
}