Two‐level salient feature complementary network for person re‐identification

International Journal of Intelligent SystemsVolume 37, Issue 9 p. 5971-5995 RESEARCH ARTICLE Two-level salient feature complementary network for person re-identification Haishun Du, Corresponding Author Haishun Du jddhs@vip.henu.edu.cn orcid.org/0000-0003-0883-8118 School of Artificial Intelligence, Henan University, Zhengzhou, China Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China Correspondence Haishun Du, School of Artificial Intelligence, Henan University, Zhengzhou 450046, China. Email: jddhs@vip.henu.edu.cnSearch for more papers by this authorZhaoyang Li, Zhaoyang Li orcid.org/0000-0003-4762-2993 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this authorPanting Liu, Panting Liu orcid.org/0000-0003-2656-1490 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this authorLinbing He, Linbing He orcid.org/0000-0001-9017-5303 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this authorDongdong Huo, Dongdong Huo orcid.org/0000-0002-9895-8529 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this author Haishun Du, Corresponding Author Haishun Du jddhs@vip.henu.edu.cn orcid.org/0000-0003-0883-8118 School of Artificial Intelligence, Henan University, Zhengzhou, China Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China Correspondence Haishun Du, School of Artificial Intelligence, Henan University, Zhengzhou 450046, China. Email: jddhs@vip.henu.edu.cnSearch for more papers by this authorZhaoyang Li, Zhaoyang Li orcid.org/0000-0003-4762-2993 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this authorPanting Liu, Panting Liu orcid.org/0000-0003-2656-1490 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this authorLinbing He, Linbing He orcid.org/0000-0001-9017-5303 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this authorDongdong Huo, Dongdong Huo orcid.org/0000-0002-9895-8529 School of Artificial Intelligence, Henan University, Zhengzhou, ChinaSearch for more papers by this author First published: 14 January 2022 https://doi.org/10.1002/int.22824Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Given a query image from a camera, person re-identification (Re-ID) can retrieve the images of the same identity from a gallery, the images of which are captured by the other cameras. Therefore, person Re-ID has been widely used in the field of video surveillance. However, person Re-ID still suffers from a series of challenges, such as illumination changes, pose variations, and occlusions. Although the person Re-ID methods based on attention mechanism give an effective and feasible solution for the above challenges, attention mechanism may make a network focus too much on the most salient discriminative features and ignore other potential discriminative features. To solve this problem, we propose a two-level salient feature complementary network (TSFC-Net) to extract the most salient discriminative features and the secondary salient discriminative features of pedestrian images for person Re-ID. Specifically, TSFC-Net first extracts the most salient discriminative features of pedestrian images by embedding the spatial and channel attention modules in the backbone network, and then extracts the secondary salient discriminative features of pedestrian images by a secondary salient feature mining module (SSFM). Since the final features of pedestrian images fuse the most salient discriminative features and the secondary salient discriminative features, TSFC-Net can significantly improve the richness and discrimination capability of pedestrian representations. In addition, we conduct extensive experiments on the Market-1501, DukeMTMC-reID, and CUHK03 data sets, and the experimental results indicate that our TSFC-Net has a better performance compared with most of the state-of-the-art person Re-ID methods. CONFLICT OF INTERESTS The authors declare that there are no conflict of interests. Volume37, Issue9September 2022Pages 5971-5995 RelatedInformation