DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person
  Recognition
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/
6825d678d5a62682c1a581ac
Kailash A. Hambarde
claimed_verified
2025-05-15T13:45:06.526Z
61c334dc77d54877f7643294
/avatars/7c68821762bcfd844c8d780b9d1276f4.svg
Hambarde
user
kailassrt
6825d678d5a62682c1a581ad
Nzakiese Mbongo
6825d678d5a62682c1a581ae
Pavan Kumar MP
6825d678d5a62682c1a581af
Satish Mekewad
6825d678d5a62682c1a581b0
Carolina Fernandes
6825d678d5a62682c1a581b1
Gökhan Silahtaroğlu
6825d678d5a62682c1a581b2
Alice Nithya
6825d678d5a62682c1a581b3
Pawan Wasnik
6825d678d5a62682c1a581b4
MD. Rashidunnabi
6825d678d5a62682c1a581b5
Pranita Samale
6825d678d5a62682c1a581b6
Hugo Proença
DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person
  Recognition
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/
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.
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