论文标题
Blazepose：设备实时身体姿势跟踪
BlazePose: On-device Real-time Body Pose tracking
论文作者
论文摘要
我们提出了Blazepose，这是一种针对人类姿势估计的轻量级卷积神经网络体系结构，该估计是针对移动设备实时推断而定制的。在推断期间，网络可为一个人提供33个身体关键点，并在Pixel 2手机上以每秒30帧的速度运行。这使其特别适合实时用例，例如健身跟踪和手语识别。我们的主要贡献包括一种新型的身体姿势跟踪解决方案和轻巧的身体姿势估计神经网络，该神经网络同时使用热图和回归到关键点坐标。
We present BlazePose, a lightweight convolutional neural 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 single 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 tracking solution and a lightweight body pose estimation neural network that uses both heatmaps and regression to keypoint coordinates.
