Robust real-time driver drowsiness detection based on image processing and feature extraction methods

2018 ◽  
Vol 10 (1) ◽  
pp. 24 ◽  
Author(s):  
Maryam Keyvanara ◽  
Nasrin Salehi ◽  
Amirhassan Monadjemi
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Real Time ◽  
Extraction Methods ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Research on Rolling Bearing Fault Feature Extraction and Diagnosis Method Based on Image Processing

2021 ◽  
Vol 5 (2) ◽  
pp. 1-7
Author(s):  
Sun Y
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Working Environment ◽  
Ring Rolling ◽  
Rolling Bearings ◽  
Binary Matrix ◽  
The Time Domain

In economic construction, there are many large and important machinery and equipment. Some equipment will continue to work in a harsh working environment, so many and various failures will occur. Rolling bearings are one of the widely used parts in rotating machinery. They are generally composed of inner ring, outer ring, rolling element and holding. The frame is composed of four parts, the failure of the bearing is particularly important, and its safe operation has a vital impact on the entire equipment, Feature extraction is the key link in the subsequent identification of fault types, Although feature extraction in the time domain and frequency domain is effective, it is also necessary to find new feature extraction methods in new areas. On the basis of the snowflake image obtained by using the principle of SDP(Symmetrized Dot Pattern), a method for extracting fault features of rolling bearings based on image processing is proposed, and the snowflake standard map for different working conditions is constructed. The number of snowflake images under different working conditions is different. The binary matrix of the test image is compared with it, and then classified and identified. Finally, the algorithm is validated, and the ideal result is obtained to verify its rationality and effectiveness.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Muhammad Tayab Khan ◽  
Hafeez Anwar ◽  
Farman Ullah ◽  
Ata Ur Rehman ◽  
Rehmat Ullah ◽  
...  
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Classification Accuracy ◽  
Eyelid Closure ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Sobel Operator ◽  
The Face ◽  
Uniform Background ◽  
The Subject

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.

Real-Time Warning System for Driver Drowsiness Detection Using Visual Information

2009 ◽  
Vol 59 (2) ◽  
pp. 103-125 ◽  
Author(s):  
Marco Javier Flores ◽  
José María Armingol ◽  
Arturo de la Escalera
Keyword(s):  
Real Time ◽  
Visual Information ◽  
Warning System ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Driver Drowsiness Detection System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2270-2272
Author(s):  
Charan M
Keyword(s):  
Real Time ◽  
Web Application ◽  
Detection System ◽  
Smart Phone ◽  
Learning Approaches ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
The Face ◽  
Bounding Boxes ◽  
Traditional Image

We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.

scholarly journals ANN analysis in a vision approach for potato inspection

2008 ◽  
Vol 6 (02) ◽  
Author(s):  
R. Rios-Cabrera ◽  
I Lopez-Juarez ◽  
Hsieh Sheng-Jen
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Comparative Analysis ◽  
Physical Properties ◽  
Real Time ◽  
The Other ◽  
Stability And Convergence

An image processing methodology for the extraction of potato properties is explained. The objective is to determine their quality evaluating physical properties and using Artificial Neural Networks (ANN’s) to find misshapen potatoes. A comparative analysis for three connectionist models (Backpropagation, Perceptron and FuzzyARTMAP), evaluating speed and stability for classifying extracted properties is presented. The methodology for image processing and pattern feature extraction is presented together with some results. These results showed that FuzzyARTMAP outperformed the other models due to its stability and convergence speed with times as low as 1 ms per pattern which demonstrates its suitability for real-time inspection. Several algorithms to determine potato defects such as greening, scab, cracks are proposed which can be affectively used for grading different quality of potatoes.

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