Smart Real-Time Video Surveillance Platform for Drowsiness Detection Based on Eyelid Closure

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原文链接

NSTL
Wiley-Hindawi

外文摘要：We propose drowsiness detection in real-time surveillance videos by determining if a person's eyes are open or closed. As a first step, the face of the subject is detected in the image. In the detected face, the eyes are localized and filtered with an extended Sobel operator to detect the curvature of the eyelids. Once the curves are detected, concavity is used to tell whether the eyelids are closed or open. Consequently, a concave upward curve means the eyelid is closed whereas a concave downwards curve means the eye is open. The proposed method is also implemented on hardware in order to be used in real-time scenarios, such as driver drowsiness detection. The evaluation of the proposed method used three image datasets, where images in the first dataset have a uniform background. The proposed method achieved classification accuracy of up to 95% on this dataset. Another benchmark dataset used has significant variations based on face deformations. With this dataset, our method achieved classification accuracy of 70%. A real-time video dataset of people driving the car was also used, where the proposed method achieved 95% accuracy, thus showing its feasibility for use in real-time scenarios.

作者：

Hafeez Anwar、Farman Ullah、Ata Ur Rehman、Rehmat Ullah、Asif Iqbal、Muhammad Tayab Khan、Bok-Hee Lee、Kyung Sup Kwak

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作者单位：

Department of Electrical and Computer Engineering, COMSATS University Islamabad

Department of Computer Systems Engineering, University of Engineering & Technology

Department of Information and Communication Engineering, Inha University

The Incubator, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

Department of Electrical Engineering, Inha University

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出版年：

2019

DOI：

10.1155/2019/2036818

Wireless communications & mobile computing

ISTP

ISSN：1530-8669

年,卷(期)：2019.2019

被引量8
参考文献量20