AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification
IEEE Transactions on Information Forensics and Security(2024)
摘要
Aerial-ground person re-identification (Re-ID) presents unique challenges in
computer vision, stemming from the distinct differences in viewpoints, poses,
and resolutions between high-altitude aerial and ground-based cameras. Existing
research predominantly focuses on ground-to-ground matching, with aerial
matching less explored due to a dearth of comprehensive datasets. To address
this, we introduce AG-ReID.v2, a dataset specifically designed for person Re-ID
in mixed aerial and ground scenarios. This dataset comprises 100,502 images of
1,615 unique individuals, each annotated with matching IDs and 15 soft
attribute labels. Data were collected from diverse perspectives using a UAV,
stationary CCTV, and smart glasses-integrated camera, providing a rich variety
of intra-identity variations. Additionally, we have developed an explainable
attention network tailored for this dataset. This network features a
three-stream architecture that efficiently processes pairwise image distances,
emphasizes key top-down features, and adapts to variations in appearance due to
altitude differences. Comparative evaluations demonstrate the superiority of
our approach over existing baselines. We plan to release the dataset and
algorithm source code publicly, aiming to advance research in this specialized
field of computer vision. For access, please visit
https://github.com/huynguyen792/AG-ReID.v2.
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关键词
Person re-identification,aerial-ground imagery,UAV,CCTV,smart glasses,video surveillance,attribute-guided,three-stream network
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