{"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/fast-and-robust-detection-of-fallen-people","title":"Fast and Robust Detection of Fallen People from a Mobile Robot","arxiv_id":"1703.03349","date":"2017-03-09","proceeding":null,"authors":["Morris Antonello","Marco Carraro","Marco Pierobon","Emanuele Menegatti"],"abstract":"This paper deals with the problem of detecting fallen people lying on the\nfloor by means of a mobile robot equipped with a 3D depth sensor. In the\nproposed algorithm, inspired by semantic segmentation techniques, the 3D scene\nis over-segmented into small patches. Fallen people are then detected by means\nof two SVM classifiers: the first one labels each patch, while the second one\ncaptures the spatial relations between them. This novel approach showed to be\nrobust and fast. Indeed, thanks to the use of small patches, fallen people in\nreal cluttered scenes with objects side by side are correctly detected.\nMoreover, the algorithm can be executed on a mobile robot fitted with a\nstandard laptop making it possible to exploit the 2D environmental map built by\nthe robot and the multiple points of view obtained during the robot navigation.\nAdditionally, this algorithm is robust to illumination changes since it does\nnot rely on RGB data but on depth data. All the methods have been thoroughly\nvalidated on the IASLAB-RGBD Fallen Person Dataset, which is published online\nas a further contribution. It consists of several static and dynamic sequences\nwith 15 different people and 2 different environments.","url_abs":"http://arxiv.org/abs/1703.03349v1","url_pdf":"http://arxiv.org/pdf/1703.03349v1.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":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[{"slug":"fpds","name":"FPDS","full_name":"Fallen People Data Set"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.03349","atlas_url":"https://app.syntology.ai/?focus=1703.03349","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}