  In this article we present a novel underwater dataset collected from several
field trials within the EU FP7 project "Cognitive autonomous diving buddy
(CADDY)", where an Autonomous Underwater Vehicle (AUV) was used to interact
with divers and monitor their activities. To our knowledge, this is one of the
first efforts to collect a large dataset in underwater environments targeting
object classification, segmentation and human pose estimation tasks. The first
part of the dataset contains stereo camera recordings (~10K) of divers
performing hand gestures to communicate and interact with an AUV in different
environmental conditions. These gestures samples serve to test the robustness
of object detection and classification algorithms against underwater image
distortions i.e., color attenuation and light backscatter. The second part
includes stereo footage (~12.7K) of divers free-swimming in front of the AUV,
along with synchronized IMUs measurements located throughout the diver's suit
(DiverNet) which serve as ground-truth for human pose and tracking methods. In
both cases, these rectified images allow investigation of 3D representation and
reasoning pipelines from low-texture targets commonly present in underwater
scenarios. In this paper we describe our recording platform, sensor calibration
procedure plus the data format and the utilities provided to use the dataset.
