{"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/articulated-clinician-detection-using-3d","title":"Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data","arxiv_id":"1602.03468","date":"2016-02-10","proceeding":null,"authors":["Abdolrahim Kadkhodamohammadi","Afshin Gangi","Michel de Mathelin","Nicolas Padoy"],"abstract":"Reliable human pose estimation (HPE) is essential to many clinical\napplications, such as surgical workflow analysis, radiation safety monitoring\nand human-robot cooperation. Proposed methods for the operating room (OR) rely\neither on foreground estimation using a multi-camera system, which is a\nchallenge in real ORs due to color similarities and frequent illumination\nchanges, or on wearable sensors or markers, which are invasive and therefore\ndifficult to introduce in the room. Instead, we propose a novel approach based\non Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in\nreal ORs. We extend the PS framework in two ways. First, we build robust and\ndiscriminative part detectors using both color and depth images. We also\npresent a novel descriptor for depth images, called histogram of depth\ndifferences (HDD). Second, we extend PS to 3D by proposing 3D pairwise\nconstraints and a new method that makes exact inference tractable. Our approach\nis evaluated for pose estimation and clinician detection on a challenging RGB-D\ndataset recorded in a busy operating room during live surgeries. We conduct\nseries of experiments to study the different part detectors in conjunction with\nthe various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D\nPS with RGB-D part detectors significantly improves the results in a visually\nchallenging operating environment.","url_abs":"http://arxiv.org/abs/1602.03468v4","url_pdf":"http://arxiv.org/pdf/1602.03468v4.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":[{"paper_slug":"articulated-clinician-detection-using-3d","repo_url":"https://github.com/CAMMA-public/mvor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}