DetReIDX：面向現實世界無人機人員識別的壓力測試數據集

DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person Recognition

May 7, 2025

作者: Kailash A. Hambarde, Nzakiese Mbongo, Pavan Kumar MP, Satish Mekewad, Carolina Fernandes, Gökhan Silahtaroğlu, Alice Nithya, Pawan Wasnik, MD. Rashidunnabi, Pranita Samale, Hugo Proença

cs.AI

摘要

人員重識別（ReID）技術在受控的地面條件下表現相對良好，但在實際應用於具有挑戰性的現實場景時卻往往失效。顯然，這是由於極端的數據變異因素，如分辨率、視角變化、尺度差異、遮擋以及服裝或時間漂移帶來的外觀變化。此外，公開可用的數據集並未真實地涵蓋這些類型和程度的變異性，這限制了該技術的進步。本文介紹了DetReIDX，一個大規模的空中-地面人員數據集，該數據集專門設計用於在現實條件下對ReID進行壓力測試。DetReIDX是一個多會話數據集，包含來自509個身份的超過1300萬個邊界框，數據收集自三大洲的七所大學校園，無人機飛行高度在5.8至120米之間。更重要的是，作為一個關鍵創新，DetReIDX中的對象在不同日期至少進行了兩次記錄，期間服裝、日光和地點均有所變化，使其真正適合評估長期人員重識別。此外，數據還標註了16個軟生物特徵屬性以及用於檢測、跟踪、重識別和動作識別的多任務標籤。為了提供DetReIDX實用性的實證證據，我們考慮了人體檢測和重識別這兩個具體任務，在DetReIDX的條件下，最先進的方法性能急劇下降（檢測準確率下降高達80%，Rank-1重識別率下降超過70%）。該數據集、註釋和官方評估協議可在https://www.it.ubi.pt/DetReIDX/公開獲取。

English

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/