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.
We present BlazePose GHUM Holistic, a lightweight neural network pipeline for 3D human body landmarks and pose estimation, specifically tailored to real-time on-device inference. BlazePose GHUM Holistic enables motion capture from a single RGB image including avatar control, fitness tracking and AR/VR effects. Our main contributions include i) a novel method for 3D ground truth data acquisition, ii) updated 3D body tracking with additional hand landmarks and iii) full body pose estimation from a monocular image.
We have introduced an approach for low optical absorption coefficient measurement of piezoelectric materials based on the piezoelectric resonance laser calorimetry (PRLC) method. An RF oscillator circuit provides a convenient way to track the piezoelectric resonance frequency during laser irradiation. In this work, the optical absorption coefficient of lithium triborate crystal under high-power IR laser irradiation was measured using the oscillator circuit and the conventional PRLC technique.
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