{"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/stargazer-a-transformer-based-driver-action","title":"Stargazer: A transformer-based driver action detection system for intelligent transportation","arxiv_id":null,"date":"2022-06-01","proceeding":"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 6","authors":["Junwei Liang","He Zhu","Enwei Zhang","Jun Zhang"],"abstract":"Distracted driver actions can be dangerous and cause severe accidents. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. To this end, we study driver action detection using videos captured inside the vehicle. We propose Stargazer, an efficient, transformer-based system exploiting rich temporal features about the human behavioral information, with a simple yet effective action temporal localization framework. The core of our system contains an improved version of the multi-scale vision transformer network, which learns a hierarchy of robust representations. We then use a sliding-window classification strategy to facilitate temporal localization of actions-of-interest. The proposed system wins the second place in the Naturalistic Driving Action Recognition of AI City Challenge 2022 (Track 3). The code and models are released.","url_abs":"https://openaccess.thecvf.com/content/CVPR2022W/AICity/html/Liang_Stargazer_A_Transformer-Based_Driver_Action_Detection_System_for_Intelligent_Transportation_CVPRW_2022_paper.html","url_pdf":"https://openaccess.thecvf.com/content/CVPR2022W/AICity/papers/Liang_Stargazer_A_Transformer-Based_Driver_Action_Detection_System_for_Intelligent_Transportation_CVPRW_2022_paper.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":"stargazer-a-transformer-based-driver-action","repo_url":"https://github.com/JunweiLiang/aicity_action","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"temporal-localization","task_name":"Temporal Localization"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}