{"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/predicting-the-drivers-focus-of-attention-the","title":"Predicting the Driver's Focus of Attention: the DR(eye)VE Project","arxiv_id":"1705.03854","date":"2017-05-10","proceeding":null,"authors":["Andrea Palazzi","Davide Abati","Simone Calderara","Francesco Solera","Rita Cucchiara"],"abstract":"In this work we aim to predict the driver's focus of attention. The goal is\nto estimate what a person would pay attention to while driving, and which part\nof the scene around the vehicle is more critical for the task. To this end we\npropose a new computer vision model based on a multi-branch deep architecture\nthat integrates three sources of information: raw video, motion and scene\nsemantics. We also introduce DR(eye)VE, the largest dataset of driving scenes\nfor which eye-tracking annotations are available. This dataset features more\nthan 500,000 registered frames, matching ego-centric views (from glasses worn\nby drivers) and car-centric views (from roof-mounted camera), further enriched\nby other sensors measurements. Results highlight that several attention\npatterns are shared across drivers and can be reproduced to some extent. The\nindication of which elements in the scene are likely to capture the driver's\nattention may benefit several applications in the context of human-vehicle\ninteraction and driver attention analysis.","url_abs":"http://arxiv.org/abs/1705.03854v3","url_pdf":"http://arxiv.org/pdf/1705.03854v3.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":"predicting-the-drivers-focus-of-attention-the","repo_url":"https://github.com/ndrplz/dreyeve","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"dr-eye-ve","name":"DR(eye)VE","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1705.03854","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}