title: DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person
    Recognition
summary: Person reidentification (ReID) technology has been considered to perform
  relatively well under controlled, ground-level conditions, but it breaks down
  when deployed in challenging real-world settings. Evidently, this is due to
  extreme data variability factors such as resolution, viewpoint changes, scale
  variations, occlusions, and appearance shifts from clothing or session drifts.
  Moreover, the publicly available data sets do not realistically incorporate
  such kinds and magnitudes of variability, which limits the progress of this
  technology. This paper introduces DetReIDX, a large-scale aerial-ground person
  dataset, that was explicitly designed as a stress test to ReID under real-world
  conditions. DetReIDX is a multi-session set that includes over 13 million
  bounding boxes from 509 identities, collected in seven university campuses from
  three continents, with drone altitudes between 5.8 and 120 meters. More
  important, as a key novelty, DetReIDX subjects were recorded in (at least) two
  sessions on different days, with changes in clothing, daylight and location,
  making it suitable to actually evaluate long-term person ReID. Plus, data were
  annotated from 16 soft biometric attributes and multitask labels for detection,
  tracking, ReID, and action recognition. In order to provide empirical evidence
  of DetReIDX usefulness, we considered the specific tasks of human detection and
  ReID, where SOTA methods catastrophically degrade performance (up to 80% in
  detection accuracy and over 70% in Rank-1 ReID) when exposed to DetReIDXs
  conditions. The dataset, annotations, and official evaluation protocols are
  publicly available at https://www.it.ubi.pt/DetReIDX/
paper_authors._id: 6825d678d5a62682c1a581ac
paper_authors.name: Kailash A. Hambarde
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-15T13:45:06.526Z
paper_authors._id: 61c334dc77d54877f7643294
paper_authors.avatarUrl: /avatars/7c68821762bcfd844c8d780b9d1276f4.svg
paper_authors.fullname: Hambarde
paper_authors.type: user
paper_authors.user: kailassrt
paper_authors._id: 6825d678d5a62682c1a581ad
paper_authors.name: Nzakiese Mbongo
paper_authors._id: 6825d678d5a62682c1a581ae
paper_authors.name: Pavan Kumar MP
paper_authors._id: 6825d678d5a62682c1a581af
paper_authors.name: Satish Mekewad
paper_authors._id: 6825d678d5a62682c1a581b0
paper_authors.name: Carolina Fernandes
paper_authors._id: 6825d678d5a62682c1a581b1
paper_authors.name: Gökhan Silahtaroğlu
paper_authors._id: 6825d678d5a62682c1a581b2
paper_authors.name: Alice Nithya
paper_authors._id: 6825d678d5a62682c1a581b3
paper_authors.name: Pawan Wasnik
paper_authors._id: 6825d678d5a62682c1a581b4
paper_authors.name: MD. Rashidunnabi
paper_authors._id: 6825d678d5a62682c1a581b5
paper_authors.name: Pranita Samale
paper_authors._id: 6825d678d5a62682c1a581b6
paper_authors.name: Hugo Proença
paper_title: DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person
    Recognition
paper_summary: Person reidentification (ReID) technology has been considered to perform
  relatively well under controlled, ground-level conditions, but it breaks down
  when deployed in challenging real-world settings. Evidently, this is due to
  extreme data variability factors such as resolution, viewpoint changes, scale
  variations, occlusions, and appearance shifts from clothing or session drifts.
  Moreover, the publicly available data sets do not realistically incorporate
  such kinds and magnitudes of variability, which limits the progress of this
  technology. This paper introduces DetReIDX, a large-scale aerial-ground person
  dataset, that was explicitly designed as a stress test to ReID under real-world
  conditions. DetReIDX is a multi-session set that includes over 13 million
  bounding boxes from 509 identities, collected in seven university campuses from
  three continents, with drone altitudes between 5.8 and 120 meters. More
  important, as a key novelty, DetReIDX subjects were recorded in (at least) two
  sessions on different days, with changes in clothing, daylight and location,
  making it suitable to actually evaluate long-term person ReID. Plus, data were
  annotated from 16 soft biometric attributes and multitask labels for detection,
  tracking, ReID, and action recognition. In order to provide empirical evidence
  of DetReIDX usefulness, we considered the specific tasks of human detection and
  ReID, where SOTA methods catastrophically degrade performance (up to 80% in
  detection accuracy and over 70% in Rank-1 ReID) when exposed to DetReIDXs
  conditions. The dataset, annotations, and official evaluation protocols are
  publicly available at https://www.it.ubi.pt/DetReIDX/
paper_ai_summary: DetReIDX is a large-scale aerial-ground person dataset designed to evaluate the robustness of person reidentification technology under real-world conditions, showcasing the limitations of existing state-of-the-art methods.
paper_ai_keywords: person reidentification ReID aerial-ground multi-session bounding boxes identities resolution viewpoint changes scale variations occlusions appearance shifts soft biometric attributes multitask labels human detection Rank-1 ReID