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arXiv · 2006.10204

BlazePose: On-device Real-time Body Pose tracking

Valentin Bazarevsky·Ivan Grishchenko·Karthik Raveendran·Tyler Zhu·Fan Zhang·Matthias Grundmann
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Abstract

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.

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Valentin Bazarevsky, Ivan Grishchenko, Karthik Raveendran, Tyler Zhu, Fan Zhang, Matthias Grundmann. 2020-06-17. BlazePose: On-device Real-time Body Pose tracking. https://arxiv.org/abs/2006.10204

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