Smart Safety Monitoring System for Vehicles-Reference-Cited by-同舟云学术

Smart Safety Monitoring System for Vehicles

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things - BDIOT 2018
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Author:

Shen Xin¹,Chen Jun-Yan¹,Lei Xiao-Chun¹,Huang Long¹,Wu Long-Sheng¹,Yin Wen-Qiang¹

Affiliation:

1. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin University of Electronic Technology, Guilin, China

Funder

Guilin University of Electronic Technology

Publisher

ACM Press

Reference8 articles.

1. World Health Organization. Global status report on road safety: WHO, 2017.http://www.who.int/violence_injury_prevention/road_safety_status/report/en/.

2. Khushaba R N, Kodagoda S and Lal S. 2011. Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Transactions on Biomedical En-gineering, 58(1): 121--131.

3. Evgeniy Abdulin and Oleg Komogortsev. 2015. User Eye Fatigue Detection via Eye Movement Behavior. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '15). ACM, New York, NY, USA, 1265--1270. DOI=https://doi.org/10.1145/2702613.2732812.

4. Chong Fu and Zhi-liang Zhu. 2009. An image denoising method by using hybrid fractal-wavelet coding. In Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human (ICIS '09). ACM, New York, NY, USA, 1324--1329. DOI=http://dx.doi.org/10.1145/1655925.1656168.

5. Shuyu Yang, Xin Song, Li Zhang and Jie Yu. 2017. The anti-fatigue driving system design based on the eye blink detect. in Seventh International Conference on Electronics and Information Engineering, International Society for Optics and Photonics, 103221R.

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