The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, and Short/Long-Term Re-Identification from Aerial Devices
IEEE Transactions on Information Forensics and Security
Over the years, unmanned aerial vehicles (UAVs) have been regarded as a potential solution to surveil public spaces, providing a cheap way for data collection, while covering large and difficult-to-reach areas. This kind of solutions can be particularly useful to detect, track and identify subjects of interest in crowds, for security/safety purposes. In this context, various datasets are publicly available, yet most of them are only suitable for evaluating detection, tracking and short-term re-identification techniques. This paper announces the free availability of the P-DESTRE dataset, the first of its kind to provide video/UAV-based data for pedestrian long-term re-identification research, with ID annotations consistent across data collected in different days. As a secondary contribution, we provide the results attained by the state-of-the-art pedestrian detection, tracking, short/long term re-identification techniques in well-known surveillance datasets, used as baselines for the corresponding effectiveness observed in the P-DESTRE data. This comparison highlights the discriminating characteristics of P-DESTRE with respect to similar sets. Finally, we identify the most problematic data degradation factors and co-variates for UAV-based automated data analysis, which should be considered in subsequent technologic/conceptual advances in this field. The dataset and the full specification of the empirical evaluation carried out are freely available at http://p-destre.di.ubi.pt/.
Kumar, S. V.Aruna; Yaghoubi, Ehsan; Das, Abhijit; Harish, B. S.; and Proenca, Hugo, "The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, and Short/Long-Term Re-Identification from Aerial Devices" (2021). Journal Articles. 2240.