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FaSRnet: a feature and semantics refinement network for human pose estimation
Regular Papers | Updated:2024-04-29
    • FaSRnet: a feature and semantics refinement network for human pose estimation

      Enhanced Publication
    • FaSRnet:用于人体姿态估计的特征和语义修正网络
    • 2023年12月1日,来自重庆大学生物工程学院的ZHONG Daidi团队和微电子与通信工程学院的ZHONG Yuanhong团队在《Frontiers of Information Technology & Electronic Engineering》杂志上发表了一篇重要论文。该研究针对多帧人体姿态估计的挑战性问题,提出了一种创新的框架,通过特征和语义层面的精细化来提升姿态估计的准确性。论文指出,由于运动模糊、视频失焦和遮挡等因素,多帧人体姿态估计一直是研究的难点。大多数现有方法主要依赖对最终热图的优化来利用帧间的时间一致性,但这种做法忽视了特征层面的精细化。为此,ZHONG团队提出了一种新的方法,不仅对齐辅助特征与当前帧特征以减少特征分布差异带来的损失,还利用注意力机制融合这两种特征。在语义层面,团队引入相邻热图间的差异信息作为辅助特征来优化当前热图。该方法在PoseTrack2017和PoseTrack2018等大型基准数据集上进行了验证,并取得了显著成效。这一研究成果不仅为人体姿态估计领域提供了新的解决方案,也为未来相关研究开辟了新方向,并为相关体系建设奠定了坚实基础。
    • Frontiers of Information Technology & Electronic Engineering   Vol. 25, Issue 4, Pages: 513-526(2024)
    • DOI:10.1631/FITEE.2200639    

      CLC: TP391
    • Published: April 2024

      Received:12 December 2022

      Accepted:27 June 2023

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  • Yuanhong ZHONG, Qianfeng XU, Daidi ZHONG, et al. FaSRnet: a feature and semantics refinement network for human pose estimation. [J]. Frontiers of Information Technology & Electronic Engineering 25(4):513-526(2024) DOI: 10.1631/FITEE.2200639.

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