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Over the last decades, the world has been witnessing growing threats to thesecurity in urban spaces, which has augmented the relevance given to visualsurveillance solutions able to detect, track and identify persons of interestin crowds. In particular, unmanned aerial vehicles (UAVs) are a potential toolfor this kind of analysis, as they provide a cheap way for data collection,cover large and difficult-to-reach areas, while reducing human staff demands.In this context, all the available datasets are exclusively suitable for thepedestrian re-identification problem, in which the multi-camera views per IDare taken on a single day, and allows the use of clothing appearance featuresfor identification purposes. Accordingly, the main contributions of this paperare two-fold: 1) we announce the UAV-based P-DESTRE dataset, which is the firstof its kind to provide consistent ID annotations across multiple days, makingit suitable for the extremely challenging problem of person search, i.e., whereno clothing information can be reliably used. Apart this feature, the P-DESTREannotations enable the research on UAV-based pedestrian detection, tracking,re-identification and soft biometric solutions; and 2) we compare the resultsattained by state-of-the-art pedestrian detection, tracking, reidentificationand search techniques in well-known surveillance datasets, to the effectivenessobtained by the same techniques in the P-DESTRE data. Such comparison enablesto identify the most problematic data degradation factors of UAV-based data foreach task, and can be used as baselines for subsequent advances in this kind oftechnology. The dataset and the full details of the empirical evaluationcarried out are freely available at http://p-destre.di.ubi.pt/.

The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices