Traffic light and moving object detection for a guide‐dog robot-Reference-Cited by-同舟云学术

Traffic light and moving object detection for a guide‐dog robot

Published:2020-07 Issue:13 Volume:2020 Page:675-678
ISSN:2051-3305
Container-title:The Journal of Engineering
language:en
Short-container-title:J. eng.

Author:

Chen Qiang¹²,Chen Yinong²,Zhu Jinhui³,De Luca Gennaro²,Zhang Mei⁴,Guo Ying⁵

Affiliation:

1. College of Computer & Information Science, Southwest UniversityChongqingPeople's Republic of China

2. School of Computing, Informatics and Decision Systems Engineering, Arizona State UniversityTempeUSA

3. School of Software Engineering, South China University of TechnologyGuangzhouPeople's Republic of China

4. School of Automation Science and Control Engineering, South China University of TechnologyGuangzhouPeople's Republic of China

5. School of Information Science and Technology, Qingdao University of Science and TechnologyQingdaoPeople's Republic of China

Funder

Fundamental Research Funds for the Central Universities

China Scholarship Council

Publisher

Institution of Engineering and Technology (IET)

Subject

General Engineering,Energy Engineering and Power Technology,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2019.1137

Reference13 articles.

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2. ChuangT.K. LinN.C. ChenJ.S.et al.: ‘Deep trail‐following robotic guide dog in pedestrian environments for people who are blind and visually impaired‐learning from virtual and real worlds’.IEEE Int. Conf. on Robotics and Automation (ICRA 2018) Brisbane Australia May 2018 pp.1–7

3. KrizhevskyA. SutskeverI. HintonG.E.: ‘Imagenet classification with deep convolutional neural networks’.Advances in Neural Information Processing Systems (NIPS 2012) Lake Tahoe USA December 2012 pp.1097–1105

4. HowardA.G. ZhuM. ChenB.et al.: ‘Mobilenets: efficient convolutional neural net‐works for mobile vision applications’ preprint arXiv:1704.04861(2017)

5. ‘Movidius’ available athttps://www.movidius.com/ accessed 30 November 2018

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