{"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/handynet-a-one-stop-solution-to-detect","title":"HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands","arxiv_id":"1804.07834","date":"2018-04-20","proceeding":null,"authors":["Akshay Rangesh","Mohan M. Trivedi"],"abstract":"Tasks related to human hands have long been part of the computer vision\ncommunity. Hands being the primary actuators for humans, convey a lot about\nactivities and intents, in addition to being an alternative form of\ncommunication/interaction with other humans and machines. In this study, we\nfocus on training a single feedforward convolutional neural network (CNN)\ncapable of executing many hand related tasks that may be of use in autonomous\nand semi-autonomous vehicles of the future. The resulting network, which we\nrefer to as HandyNet, is capable of detecting, segmenting and localizing (in\n3D) driver hands inside a vehicle cabin. The network is additionally trained to\nidentify handheld objects that the driver may be interacting with. To meet the\ndata requirements to train such a network, we propose a method for cheap\nannotation based on chroma-keying, thereby bypassing weeks of human effort\nrequired to label such data. This process can generate thousands of labeled\ntraining samples in an efficient manner, and may be replicated in new\nenvironments with relative ease.","url_abs":"http://arxiv.org/abs/1804.07834v2","url_pdf":"http://arxiv.org/pdf/1804.07834v2.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":"handynet-a-one-stop-solution-to-detect","repo_url":"https://github.com/arangesh/HandyNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}