{"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/forward-propagation-backward-regression-and","title":"Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Mingzhen Huang","Supreeth Narasimhaswamy","Saif Vazir","Haibin Ling","Minh Hoai"],"abstract":"    We propose HandLer, a novel convolutional architecture that can jointly detect and track hands online in unconstrained videos. HandLer is based on Cascade-RCNNwith additional three novel stages. The first stage is Forward Propagation, where the features from frame t-1 are propagated to frame t based on previously detected hands and their estimated motion. The second stage is the Detection and Backward Regression, which uses outputs from the forward propagation to detect hands for frame t and their relative offset in frame t-1. The third stage uses an off-the-shelf human pose method to link any fragmented hand tracklets. We train the forward propagation and backward regression and detection stages end-to-end together with the other Cascade-RCNN components.To train and evaluate HandLer, we also contribute YouTube-Hand, the first challenging large-scale dataset of unconstrained videos annotated with hand locations and their trajectories. Experiments on this dataset and other benchmarks show that HandLer outperforms the existing state-of-the-art tracking algorithms by a large margin.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Huang_Forward_Propagation_Backward_Regression_and_Pose_Association_for_Hand_Tracking_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_Forward_Propagation_Backward_Regression_and_Pose_Association_for_Hand_Tracking_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":"forward-propagation-backward-regression-and","repo_url":"https://github.com/cvlab-stonybrook/HandLer","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[{"slug":"youtube-hands","name":"YouTube-Hands","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/multiple-object-tracking-on-youtube-hands","task":"Multiple Object Tracking","dataset":"YouTube-Hands","model":"HandLer","rank_in_archive_order":1,"of":1,"metrics":{"HOTA":"59.4","MOTA":"70.0"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}