查看“动捕小组/计算机视觉/BlazePose: On-device Real-time Body Pose Tracking”的源代码

{{文章推荐|Reason=轻量级二维人体姿态估计经典方法，具有算力资源要求低、准确度高等特点，可部署在笔记本电脑、移动手机等边缘设备上，当下被广泛应用于使用轻量级二维人体姿态估计方法实现各类运动分析的应用中。|Journal=CVPR Workshop on Computer Vision for Augmented and Virtual Reality, Seattle, WA, USA, 2020|PubYear=2020|DOI=10.48550/arXiv.2006.10204|Category_=研究性工作|Domain=计算机视觉|RecomBy=阿璐思|RecomGrp=动捕小组|ReviewBy=王伟明 张琛|Abstract=We present BlazePose, a lightweight convolutional neu-
ral network architecture for human pose estimation that is
tailored for real-time inference on mobile devices. During
inference, the network produces 33 body keypoints for a sin-
gle person and runs at over 30 frames per second on a Pixel
2 phone. This makes it particularly suited to real-time use
cases like fitness tracking and sign language recognition.
Our main contributions include a novel body pose track-
ing solution and a lightweight body pose estimation neural
network that uses both heatmaps and regression to keypoint
coordinates.
|DetaialsDM=人体姿态估计-二维|Citation_=467|CitationBy=/scholar?cites=9252721448284906922&amp;as_sdt=2005&amp;sciodt=0,5&amp;hl=en}}