Convolutional feature transfer via camera-specific discriminative pooling for person re-identification

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Modern Convolutional Neural Networks (CNNs) have been improving the accuracy of person re-identification (re-id) using a large number of training samples. Such a re-id system suffers from a lack of training samples for deployment to practical security applications. To address this problem, we focus on the approach that transfers features of a CNN pre-trained on a large-scale person re-id dataset to a small-scale dataset. Most of the existing CNN feature transfer methods use the features of fully connected layers that entangle locally pooled features of different spatial locations on an image. Unfortunately, due to the difference of view angles and the bias of walking directions of the persons, each camera view in a dataset has a unique spatial property in the person image, which reduces the generality of the local pooling for different cameras/datasets. To account for the camera- and dataset-specific spatial bias, we propose a method to learn camera and dataset-specific position weight maps for discriminative local pooling of convolutional features. Our experiments on four public datasets confirm the effectiveness of the proposed feature transfer with a small number of training samples in the target datasets.

本文言語英語
ホスト出版物のタイトルProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
出版社Institute of Electrical and Electronics Engineers Inc.
ページ8408-8415
ページ数8
ISBN(電子版)9781728188089
DOI
出版ステータス出版済み - 2020
イベント25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, イタリア
継続期間: 1月 10 20211月 15 2021

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

会議

会議25th International Conference on Pattern Recognition, ICPR 2020
国/地域イタリア
CityVirtual, Milan
Period1/10/211/15/21

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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