Journal
2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME
Volume -, Issue -, Pages 2585-2590Publisher
IEEE
DOI: 10.1109/ICME55011.2023.00440
Keywords
person re-id; aerial-ground imagery; uav; video surveillance; attribute-guided; two-stream network
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This study proposes a new benchmark dataset, AG-ReID, for person re-identification across aerial and ground cameras. The dataset, collected by a UAV and a ground-based CCTV camera, presents a novel elevated-viewpoint challenge and employs an explainable algorithm to address it.
Person re-ID matches persons across multiple non-overlapping cameras. Despite the increasing deployment of airborne platforms in surveillance, current existing person re-ID benchmarks' focus is on ground-ground matching and very limited efforts on aerial-aerial matching. We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus. Our dataset presents a novel elevated-viewpoint challenge for person re-ID due to the significant difference in person appearance across these cameras. We propose an explainable algorithm to guide the person re-ID model's training with soft attributes to address this challenge. Experiments demonstrate the efficacy of our method on the aerial-ground person re-ID task. The dataset will be published and the baseline codes will be open-sourced at https://github.com/huynguyen792/AG-ReID to facilitate research in this area.
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