  Unmanned Aerial Vehicles (UAVs) are of crucial importance in search and
rescue missions in maritime environments due to their flexible and fast
operation capabilities. Modern computer vision algorithms are of great interest
in aiding such missions. However, they are dependent on large amounts of
real-case training data from UAVs, which is only available for traffic
scenarios on land. Moreover, current object detection and tracking data sets
only provide limited environmental information or none at all, neglecting a
valuable source of information. Therefore, this paper introduces a large-scaled
visual object detection and tracking benchmark (SeaDronesSee) aiming to bridge
the gap from land-based vision systems to sea-based ones. We collect and
annotate over 54,000 frames with 400,000 instances captured from various
altitudes and viewing angles ranging from 5 to 260 meters and 0 to 90 degrees
while providing the respective meta information for altitude, viewing angle and
other meta data. We evaluate multiple state-of-the-art computer vision
algorithms on this newly established benchmark serving as baselines. We provide
an evaluation server where researchers can upload their prediction and compare
their results on a central leaderboard
