{"url":"/dataset/detreidx","name":"DetReIDX","full_name":"A Stress-Test Dataset for Real-World UAV-Based Person Recognition","description_markdown":"We are proud to introduce DetReIDX - is a new benchmark dataset built for real-world, long-range human recognition. It supports key computer vision tasks like person detection, re-identification (ReID), multi-view tracking, and action recognition — all captured in complex outdoor scenes using drones and ground cameras.\r\n\r\nThe dataset starts with an indoor session where each person is photographed from three angles — left, front, and right — and recorded while walking, enabling motion-based recognition like gait analysis.\r\n\r\nIn the outdoor sessions, drones capture videos from 18 viewpoints per subject, across different heights (up to 120m), distances, and camera angles (30°, 60°, 90°). Each person wears different outfits across sessions to simulate real-world variation.\r\n\r\nEvery frame is labeled with bounding boxes and 16 soft biometric attributes (like age, gender, clothing, action), offering fine-grained details for deep analysis.\r\n\r\nWith over 13+ million annotations and rich visual diversity, DetReIDX sets a new standard for evaluating human-centric AI in aerial and surveillance scenarios.","description_withheld":null,"homepage":"https://www.it.ubi.pt/DetReIDX/","introduced_date":"2025-05-07","introduced_date_note":null,"introduced_by":null,"license":{"name":"Free to use","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["DetReIDX"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}