{"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/190411953","title":"Temporal Unet: Sample Level Human Action Recognition using WiFi","arxiv_id":"1904.11953","date":"2019-04-19","proceeding":null,"authors":["Fei Wang","Yunpeng Song","Jimuyang Zhang","Jinsong Han","Dong Huang"],"abstract":"Human doing actions will result in WiFi distortion, which is widely explored\nfor action recognition, such as the elderly fallen detection, hand sign\nlanguage recognition, and keystroke estimation. As our best survey, past work\nrecognizes human action by categorizing one complete distortion series into one\naction, which we term as series-level action recognition. In this paper, we\nintroduce a much more fine-grained and challenging action recognition task into\nWiFi sensing domain, i.e., sample-level action recognition. In this task, every\nWiFi distortion sample in the whole series should be categorized into one\naction, which is a critical technique in precise action localization,\ncontinuous action segmentation, and real-time action recognition. To achieve\nWiFi-based sample-level action recognition, we fully analyze approaches in\nimage-based semantic segmentation as well as in video-based frame-level action\nrecognition, then propose a simple yet efficient deep convolutional neural\nnetwork, i.e., Temporal Unet. Experimental results show that Temporal Unet\nachieves this novel task well. Codes have been made publicly available at\nhttps://github.com/geekfeiw/WiSLAR.","url_abs":"http://arxiv.org/abs/1904.11953v1","url_pdf":"http://arxiv.org/pdf/1904.11953v1.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":"190411953","repo_url":"https://github.com/geekfeiw/WiSLAR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-localization","task_name":"Action Localization"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-segmentation","task_name":"Action Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}