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AG-ReID 2023: Aerial-Ground Person Re-identification Challenge Results
Conference proceeding   Open access

AG-ReID 2023: Aerial-Ground Person Re-identification Challenge Results

Kien Nguyen, Clinton Fookes, Sridha Sridharan, Feng Liu, Xiaoming Liu, Arun Ross, Dana Michalski, Huy Nguyen, Debayan Deb, Mahak Kothari, …
2023 IEEE International Joint Conference on Biometrics (IJCB), pp 1-10
01 Jan 2023
url
https://eprints.qut.edu.au/247688/1/165629438.pdfView
Published, Version of Record (VoR)CC BY-NC V4.0 Open

Abstract

aerial surveillance AG-ReID2023 benchmark Benchmark testing Biometrics (access control) Cameras IJCB challenge Labeling Lighting person re-identification Research and development Training
Person re-identification (Re-ID) on aerial-ground platforms has emerged as an intriguing topic within computer vision, presenting a plethora of unique challenges. Highflying altitudes of aerial cameras make persons appear differently in terms of viewpoints, poses, and resolution compared to the images of the same person viewed from ground cameras. Despite its potential, few algorithms have been developed for person re-identification on aerial-ground data, mainly due to the absence of comprehensive datasets. In response, we have collected a large-scale dataset and organized the Aerial-Ground person Re-IDentification Challenge (AG-ReID2023) to foster advancements in the field. The dataset comprises 100,502 images with 1,615 unique identities, including 51,530 training images featuring 807 identities. The test set is divided into two subsets: Aerial to Ground (808 ids, 4,348 query images, 19,259 gallery images) and Ground to Aerial (808 ids, 4,151 query images, 21,214 gallery images). In addition, we manually annotate individuals with their matching IDs across cameras and provide 15 soft attribute labels. The AG-ReID2023 Challenge in conjunction with the 7 th IEEE International Joint Conference on Biometrics (IJCB) has garnered interest from numerous institutes, resulting in the submission of five distinct algorithms. We provide an in-depth examination of the evaluation outcomes and present our findings from the contest. For additional details, kindly refer to the official website 1 . 1 https://agreid23.github.io.

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Collaboration types
Industry collaboration
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Web of Science research areas
Computer Science, Artificial Intelligence
Imaging Science & Photographic Technology
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