Eliminating Background-Bias for Robust Person Re-Identification

Maoqing Tian, Shuai Yi, Hongsheng Li, Shihua Li, Xuesen Zhang, Jianping Shi, Junjie Yan, Xiaogang Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5794-5803

Abstract


Person re-identification is an important topic in intelligent surveillance and computer vision. It aims to accurately measure visual similarities between person images for determining whether two images correspond to the same person. State-of-the-art methods mainly utilize deep learning based approaches for learning visual features for describing person appearances. However, we observe that existing deep learning models are biased to capture too much relevance between background appearances of person images. We design a series of experiments with newly created datasets to validate the influence of background information. To solve the background bias problem, we propose a person-region guided pooling deep neural network based on human parsing maps to learn more discriminative person-part features, and propose to augment training data with person images with random background. Extensive experiments demonstrate the robustness and effectiveness of our proposed method.

Related Material


[pdf]
[bibtex]
@InProceedings{Tian_2018_CVPR,
author = {Tian, Maoqing and Yi, Shuai and Li, Hongsheng and Li, Shihua and Zhang, Xuesen and Shi, Jianping and Yan, Junjie and Wang, Xiaogang},
title = {Eliminating Background-Bias for Robust Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}