MOBDrone: a Drone Video Dataset for Man OverBoard Rescue
- URL: http://arxiv.org/abs/2203.07973v1
- Date: Tue, 15 Mar 2022 15:02:23 GMT
- Title: MOBDrone: a Drone Video Dataset for Man OverBoard Rescue
- Authors: Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio
Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko
Passera and Fabrizio Falchi
- Abstract summary: We release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions.
We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes.
We conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.
- Score: 4.393945242867356
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an
essential role in speeding up the identification and rescue of people who have
fallen overboard, i.e., man overboard (MOB). To this end, Artificial
Intelligence techniques can be leveraged for the automatic understanding of
visual data acquired from drones. However, detecting people at sea in aerial
imagery is challenging primarily due to the lack of specialized annotated
datasets for training and testing detectors for this task. To fill this gap, we
introduce and publicly release the MOBDrone benchmark, a collection of more
than 125K drone-view images in a marine environment under several conditions,
such as different altitudes, camera shooting angles, and illumination. We
manually annotated more than 180K objects, of which about 113K man overboard,
precisely localizing them with bounding boxes. Moreover, we conduct a thorough
performance analysis of several state-of-the-art object detectors on the
MOBDrone data, serving as baselines for further research.
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