Abstract
Computer vision is a dynamic field, with many applications in a wide variety of industries. Choosing a platform to deploy computer vision algorithms is a complex task, with a massive amount of choice, varying in operating system, computing power, and physical size. This paper aims to measure common computer vision algorithms on a Raspberry Pi 3, helping to clarify some performance measurements and provide a clearer image of the Raspberry Pi’s viability for common computer vision operations, as well as recommend some platforms for specific algorithms.
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Acknowledgments
This work is supported by the IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) - a member of the IBM Cognitive Horizon Network, the Applications Driving Architectures (ADA) Research Center - one of the JUMP Centers co-sponsored by SRC and DARPA, and the Kellogg Honors College at California Polytechnic State University, Pomona.
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Worsley, K., Eddin, A.S., Xiong, J., Hwu, Wm., El-Hadedy, M. (2021). Performance Analysis of Computer Vision with Machine Learning Algorithms on Raspberry Pi 3. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. FTC 2020. Advances in Intelligent Systems and Computing, vol 1288. Springer, Cham. https://doi.org/10.1007/978-3-030-63128-4_17
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