DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person
  Recognition
Authors: name: Kailash A. Hambarde, name: Nzakiese Mbongo, name: Pavan Kumar MP, name: Satish Mekewad, name: Carolina Fernandes, name: Gökhan Silahtaroğlu, name: Alice Nithya, name: Pawan Wasnik, name: MD. Rashidunnabi, name: Pranita Samale, name: Hugo Proença

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/