{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/caddy-underwater-stereo-vision-dataset-for","title":"CADDY Underwater Stereo-Vision Dataset for Human-Robot Interaction (HRI) in the Context of Diver Activities","arxiv_id":"1807.04856","date":"2018-07-12","proceeding":null,"authors":["Arturo Gomez Chavez","Andrea Ranieri","Davide Chiarella","Enrica Zereik","Anja Babić","Andreas Birk"],"abstract":"In this article we present a novel underwater dataset collected from several\nfield trials within the EU FP7 project \"Cognitive autonomous diving buddy\n(CADDY)\", where an Autonomous Underwater Vehicle (AUV) was used to interact\nwith divers and monitor their activities. To our knowledge, this is one of the\nfirst efforts to collect a large dataset in underwater environments targeting\nobject classification, segmentation and human pose estimation tasks. The first\npart of the dataset contains stereo camera recordings (~10K) of divers\nperforming hand gestures to communicate and interact with an AUV in different\nenvironmental conditions. These gestures samples serve to test the robustness\nof object detection and classification algorithms against underwater image\ndistortions i.e., color attenuation and light backscatter. The second part\nincludes stereo footage (~12.7K) of divers free-swimming in front of the AUV,\nalong with synchronized IMUs measurements located throughout the diver's suit\n(DiverNet) which serve as ground-truth for human pose and tracking methods. In\nboth cases, these rectified images allow investigation of 3D representation and\nreasoning pipelines from low-texture targets commonly present in underwater\nscenarios. In this paper we describe our recording platform, sensor calibration\nprocedure plus the data format and the utilities provided to use the dataset.","url_abs":"http://arxiv.org/abs/1807.04856v1","url_pdf":"http://arxiv.org/pdf/1807.04856v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"caddy","name":"CADDY","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}