{"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/pose-estimator-and-tracker-using-temporal","title":"Pose estimator and tracker using temporal flow maps for limbs","arxiv_id":"1905.09500","date":"2019-05-23","proceeding":null,"authors":["Jihye Hwang","Jieun Lee","Sungheon Park","Nojun Kwak"],"abstract":"For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flow maps are unit vectors describing the limbs' movements. We constructed a network to learn both spatial information and temporal information end-to-end. Spatial information such as joint heatmaps and part affinity fields is regressed in the spatial network part, and the TML is regressed in the temporal network part. We also propose a data augmentation method to learn various types of TML better. The proposed multi-stride method expands the data by randomly selecting two frames within a defined range. We demonstrate that the proposed method efficiently estimates and tracks human poses on the PoseTrack 2017 and 2018 datasets.","url_abs":"https://arxiv.org/abs/1905.09500v1","url_pdf":"https://arxiv.org/pdf/1905.09500v1.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":[],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"pose-tracking","task_name":"Pose Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pose-tracking-on-posetrack2017","task":"Pose Tracking","dataset":"PoseTrack2017","model":"TML++ (MIPAL)","rank_in_archive_order":6,"of":10,"metrics":{"MOTA":"54.46","mAP":"68.78"},"uses_additional_data":false},{"leaderboard":"/sota/pose-tracking-on-posetrack2018","task":"Pose Tracking","dataset":"PoseTrack2018","model":"TML++ (MIPAL)","rank_in_archive_order":5,"of":5,"metrics":{"MOTA":"54.86","mAP":"67.81"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1905.09500","atlas_url":"https://app.syntology.ai/?focus=1905.09500","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}