@inproceedings{bcd4cd2f30f64dd4825ac513887f068a,
title = "Performance Analysis of Computer Vision with Machine Learning Algorithms on Raspberry Pi 3",
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{\textquoteright}s viability for common computer vision operations, as well as recommend some platforms for specific algorithms.",
keywords = "Computer vision, Face detection, Haar, Object detection, Performance, Raspberry Pi 3, YOLO, dlib",
author = "Kevin Worsley and Eddin, {Anas Salah} and Jinjun Xiong and Hwu, {Wen mei} and Mohamed El-Hadedy",
note = "Funding Information: 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. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; Future Technologies Conference, FTC 2020 ; Conference date: 05-11-2020 Through 06-11-2020",
year = "2021",
doi = "10.1007/978-3-030-63128-4_17",
language = "English (US)",
isbn = "9783030631277",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "216--232",
editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",
booktitle = "Proceedings of the Future Technologies Conference, FTC 2020, Volume 1",
address = "Germany",
}