The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection,
Tracking, Re-Identification and Search from Aerial Devices
- URL: http://arxiv.org/abs/2004.02782v1
- Date: Mon, 6 Apr 2020 16:17:32 GMT
- Title: The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection,
Tracking, Re-Identification and Search from Aerial Devices
- Authors: S.V. Aruna Kumar, Ehsan Yaghoubi, Abhijit Das, B.S. Harish and Hugo
Proen\c{c}a
- Abstract summary: This paper introduces the P-DESTRE dataset, which is the first of its kind to provide consistent ID annotations across multiple days.
We also compare the results attained by state-of-the-art pedestrian detection, tracking, reidentification and search techniques in well-known surveillance datasets.
The dataset and the full details of the empirical evaluation carried out are freely available at http://p-destre.di.ubi.pt/.
- Score: 7.095987222706225
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the last decades, the world has been witnessing growing threats to the
security in urban spaces, which has augmented the relevance given to visual
surveillance solutions able to detect, track and identify persons of interest
in crowds. In particular, unmanned aerial vehicles (UAVs) are a potential tool
for 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 the
pedestrian re-identification problem, in which the multi-camera views per ID
are taken on a single day, and allows the use of clothing appearance features
for identification purposes. Accordingly, the main contributions of this paper
are two-fold: 1) we announce the UAV-based P-DESTRE dataset, which is the first
of its kind to provide consistent ID annotations across multiple days, making
it suitable for the extremely challenging problem of person search, i.e., where
no clothing information can be reliably used. Apart this feature, the P-DESTRE
annotations enable the research on UAV-based pedestrian detection, tracking,
re-identification and soft biometric solutions; and 2) we compare the results
attained by state-of-the-art pedestrian detection, tracking, reidentification
and search techniques in well-known surveillance datasets, to the effectiveness
obtained by the same techniques in the P-DESTRE data. Such comparison enables
to identify the most problematic data degradation factors of UAV-based data for
each task, and can be used as baselines for subsequent advances in this kind of
technology. The dataset and the full details of the empirical evaluation
carried out are freely available at http://p-destre.di.ubi.pt/.
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