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arXiv · 1906.08172

MediaPipe: A Framework for Building Perception Pipelines

Camillo Lugaresi·Jiuqiang Tang·Hadon Nash·Chris McClanahan·Esha Uboweja·Michael Hays·Fan Zhang·Chuo-Ling Chang·Ming Guang Yong·Juhyun Lee·Wan-Teh Chang·Wei Hua·Manfred Georg·Matthias Grundmann
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Abstract

Building applications that perceive the world around them is challenging. A developer needs to (a) select and develop corresponding machine learning algorithms and models, (b) build a series of prototypes and demos, (c) balance resource consumption against the quality of the solutions, and finally (d) identify and mitigate problematic cases. The MediaPipe framework addresses all of these challenges. A developer can use MediaPipe to build prototypes by combining existing perception components, to advance them to polished cross-platform applications and measure system performance and resource consumption on target platforms. We show that these features enable a developer to focus on the algorithm or model development and use MediaPipe as an environment for iteratively improving their application with results reproducible across different devices and platforms. MediaPipe will be open-sourced at https://github.com/google/mediapipe.

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Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, Matthias Grundmann. 2019-06-14. MediaPipe: A Framework for Building Perception Pipelines. https://arxiv.org/abs/1906.08172

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