DetReIDX：面向现实世界无人机人员识别的压力测试数据集

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

行人重识别（ReID）技术在受控的地面条件下表现相对良好，但在实际复杂场景中部署时却效果不佳。显然，这是由于极端的数据变化因素所致，如分辨率、视角变化、尺度差异、遮挡以及服装或时段漂移引起的外观变化。此外，公开可用的数据集并未真实地包含此类变化及其程度，这限制了该技术的发展。本文介绍了DetReIDX，一个大规模的空地行人数据集，专为在现实世界条件下对ReID进行压力测试而设计。DetReIDX是一个多时段数据集，包含来自509个身份的超过1300万个边界框，数据采集自三大洲的七所大学校园，无人机飞行高度介于5.8至120米之间。更重要的是，作为一项关键创新，DetReIDX中的对象至少在不同日期的两个时段被记录，期间服装、光照和地点均有所变化，使其真正适用于评估长期行人重识别。此外，数据还标注了16个软生物特征属性以及用于检测、跟踪、重识别和动作识别的多任务标签。为了提供DetReIDX实用性的实证证据，我们考察了人体检测和重识别这两个具体任务，在这些任务中，当面对DetReIDX的条件时，最先进的方法性能显著下降（检测准确率下降高达80%，Rank-1重识别率下降超过70%）。该数据集、标注及官方评估协议已在https://www.it.ubi.pt/DetReIDX/公开提供。

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/