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2020

BlazePose: On-device Real-time Body Pose tracking

Bazarevsky, Valentin, Grishchenko, Ivan, Raveendran, Karthik et al.

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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.

BlazePose: On-device Real-time Body Pose tracking · Around

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Valentin Bazarevsky, Yury Kartynnik, Andrey Vakunov, Karthik Raveendran, and Matthias Grundmann · 2019
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Z Cao, G Martinez Hidalgo, T Simon, SE Wei, and YA Sheikh · 2019
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