Consistent-Aware Deep Learning for Person Re-Identification in a Camera Network

Ji Lin, Liangliang Ren, Jiwen Lu, Jianjiang Feng, Jie Zhou; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5771-5780

Abstract


In this paper, we propose a consistent-aware deep learning (CADL) framework for person re-identification in a camera network. Unlike most existing person re-identification methods which identify whether two body images are from the same person, our approach aims to obtain the maximal correct matches for the whole camera network. Different from recently proposed camera network based re-identification methods which only consider the consistent information in the matching stage to obtain a global optimal association, we exploit such consistent-aware information under a deep learning framework where both feature representation and image matching are automatically learned with certain consistent constraints. Specifically, we reach the global optimal solution and balance the performance between different cameras by optimizing the similarity and association iteratively. Experimental results show that our method obtains significant performance improvement and outperforms the state-of-the-art methods by large margins.

Related Material


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[bibtex]
@InProceedings{Lin_2017_CVPR,
author = {Lin, Ji and Ren, Liangliang and Lu, Jiwen and Feng, Jianjiang and Zhou, Jie},
title = {Consistent-Aware Deep Learning for Person Re-Identification in a Camera Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}