{"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/ld-congr-a-large-rgb-d-video-dataset-for-long","title":"LD-ConGR: A Large RGB-D Video Dataset for Long-Distance Continuous Gesture Recognition","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Dan Liu","Libo Zhang","Yanjun Wu"],"abstract":"    Gesture recognition plays an important role in natural human-computer interaction and sign language recognition. Existing research on gesture recognition is limited to close-range interaction such as vehicle gesture control and face-to-face communication. To apply gesture recognition to long-distance interactive scenes such as meetings and smart homes, a large RGB-D video dataset LD-ConGR is established in this paper. LD-ConGR is distinguished from existing gesture datasets by its long-distance gesture collection, fine-grained annotations, and high video quality. Specifically, 1) the farthest gesture provided by the LD-ConGR is captured 4m away from the camera while existing gesture datasets collect gestures within 1m from the camera; 2) besides the gesture category, the temporal segmentation of gestures and hand location are also annotated in LD-ConGR; 3) videos are captured at high resolution (1280x720 for color streams and 640x576 for depth streams) and high frame rate (30 fps). On top of the LD-ConGR, a series of experimental and studies are conducted, and the proposed gesture region estimation and key frame sampling strategies are demonstrated to be effective in dealing with long-distance gesture recognition and the uncertainty of gesture duration. The dataset and experimental results presented in this paper are expected to boost the research of long-distance gesture recognition. The dataset is available at https://github.com/Diananini/LD-ConGR-CVPR2022.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Liu_LD-ConGR_A_Large_RGB-D_Video_Dataset_for_Long-Distance_Continuous_Gesture_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_LD-ConGR_A_Large_RGB-D_Video_Dataset_for_Long-Distance_Continuous_Gesture_CVPR_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":"ld-congr-a-large-rgb-d-video-dataset-for-long","repo_url":"https://github.com/diananini/ld-congr-cvpr2022","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}