scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Safe driving by detecting lane discipline and driver drowsiness

08 May 2014-pp 1008-1012
TL;DR: The main focus is on the fatigue of the driver and his maintenance of lane discipline, mainly on-Drunk driving or drowsiness and lane discipline.
Abstract: In the modern day world, road accidents have become very common. They not only cause damage to property, but also keep at risk the lives of people travelling. Road safety is an issue of national concern, looking at its magnitude and the incidental negative impacts on the economy, public health, safety and the general welfare of the people. These road accidents may be due to many reasons like rash driving, drink and driving, inexperience, jumping signals, ignoring signboards. Since, road accidents is an important issue to be addressed, this paper will be concentrating on avoiding the road accidents by concentrating mainly on-Drunk driving or drowsiness and lane discipline. The paper has two parts. Firstly, lane detection using Hough Transform. Secondly, eye detection of driver for drowsiness detection. Thus, the main focus is on the fatigue of the driver and his maintenance of lane discipline.
Citations
More filters
Journal ArticleDOI
TL;DR: Overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.
Abstract: Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.

174 citations


Additional excerpts

  • ...[16] proposed the Drivers’ Drowsiness Detection system....

    [...]

Proceedings ArticleDOI
01 Nov 2017
TL;DR: A method to detect drunk driving patterns using only basic car sensors, available through off the shelf OBD-II dongles, makes use of a machine learning algorithm (Logistic Regression) for classification to achieve an accuracy of 82%.
Abstract: In this paper, we propose a method to detect drunk driving patterns using only basic car sensors, available through off the shelf OBD-II dongles. The sensor data include standard OnBoard Diagnostic sensor information along with an accelerometer sensor and GPS coordinates which are provided by the dongle. We collect the information through drive tests of normal driving behavior and controlled drunk driving behavior. The controlled driving emulation reflects the most common cues relating to drunk driving. After datasets are collected, a window-based approached was used for data smoothing and feature extraction. Finally, our approach makes use of a machine learning algorithm (Logistic Regression) for classification to achieve an accuracy of 82%.

16 citations


Cites background from "Safe driving by detecting lane disc..."

  • ...From the above research, we can observe that the drunk driving behavior was being predicted using external sensors such as phone sensors [8] or cameras/breath analyzers [3] [4] [5] [15]....

    [...]

  • ...Other works and projects used eye movement detection through a camera, like in [5]....

    [...]

Proceedings ArticleDOI
01 Apr 2019
TL;DR: The techniques to monitor driver safety by evaluating information associated to the visual features of the driver will help in the accident alerting and speed reducing is not complicated and reduced speed will have intimated to the driver.
Abstract: Accidents are becoming very common in nowadays. It’s causes due to lack of attention of driver while driving. This makes heavy losses and lot of death. These accidents are harm to environment. In this paper, we are describing a module for alert for driver using simple monitoring system. In the analysis, it exposes more than 25% of the accident are caused due to the driver drowsiness. This assures that drowsiness is more harm than drunk and driving. Detection of driver drowsiness is done with simple camera system. This starts the monitoring now of car started and up to end. This monitoring system related to the speed control system which was like automatic speed controller. This paper expresses the techniques to monitor driver safety by evaluating information associated to the visual features of the driver. This will help in the accident alerting. Thus, speed reducing is not complicated and reduced speed will have intimated to the driver. Finally, the accidents happened due to drowsiness of the driver that is reduced by our proposed method.

9 citations


Cites background from "Safe driving by detecting lane disc..."

  • ...A few creatures, for example, the mantis shrimp can see utilizing significantly more of the infrared and additionally bright range than people [5]....

    [...]

Book ChapterDOI
TL;DR: The algorithm was developed as a combination of concepts like HSV, thresholding, Canny edge detection, and random sample consensus (RANSAC) algorithm and was able to identify the road edge at various light conditions and various vehicle speeds.
Abstract: A report published by Ministry of Road Transport and Highways, Government of India, claims Mohan (IATSS Res 33:75–79, 2009 [1]) that around 17 deaths happen every hour by road accidents. Driver negligence is one of the major contributors to road accidents. Deviating from the road and hitting roadside objects can be avoided with early warning systems. Lane departure warning systems are inadequate to find the road edges, because of its indefinite nature. In this paper, an efficient algorithm has been proposed to identify the road edges. The algorithm was developed as a combination of concepts like HSV, thresholding, Canny edge detection, and random sample consensus (RANSAC) algorithm. Initially, the sample dataset was used to validate the algorithm. In the second iteration, real-time video was used to validate the algorithm. The algorithm was able to identify the road edge at various light conditions and various vehicle speeds. The algorithm was also developed further to calculate the distance from the center line and the road width.

6 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: Some of the recent works in this field of drowsiness detection are reviewed, some new applications are introduced and the general steps to implementing each of them are shown.
Abstract: Over the last few years, Drowsiness Detection has gained much attention in the field of artificial intelligence as well as deep neural networks in computer science. One of its excellent applications is the Drivers Drowsiness Detection, Drowsiness Detection is useful for many tasks and the application of deep learning in drowsiness detection is increasingly developing. We review some of the recent works in this field, introduce some new applications and show the general steps to implementing each of them.

6 citations


Additional excerpts

  • ...[14] proposed the Drivers’ Drowsiness Detection system....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: An improved face region extraction algorithm and a light dots detection algorithm are proposed for better eye detection performance.

92 citations


"Safe driving by detecting lane disc..." refers methods in this paper

  • ...The commonly used approaches for passive eye detection include the template matching method, Eigen space method, and Hough transform-based method [5] [6], [4]....

    [...]

Proceedings ArticleDOI
06 Mar 2009
TL;DR: This paper presents an eye detection approach using Circular Hough transform that relies primarily on the circular shape of the eye in two-dimensional image to detect the eye pair on the image precisely.
Abstract: This paper presents an eye detection approach using Circular Hough transform. Assuming the face region has already been detected by any of the accurate existing face detection methods, the search of eye pair relies primarily on the circular shape of the eye in two-dimensional image. The eyes detection process includes preprocessing that filtered and cropped the face images and Circular Hough Transform is used to detect the circular shape of the eye and to mark the eye pair on the image precisely. This eyes detection method was tested on Face DB database developed by Park Lab, University of Illinois at Urbana Champaign USA. Most of the faces are frontal with open eyes and some are tilted upwards or downwards. The detection accuracy of the proposed method is about 86%.

54 citations

Proceedings ArticleDOI
25 Sep 2012
TL;DR: A fast and improved algorithm with the ability to detect unexpected lane changes is aimed in this paper which first defines the region of interest from input image for reducing searching space, and divided the image into near field of view and far field of views.
Abstract: A fast and improved algorithm with the ability to detect unexpected lane changes is aimed in this paper. A short segment of a long curve has relative low curvature which is approximated as a straight line. Based on the characteristics of physical road lane, this paper presents a lane detection technique based on H-MAXIMA transformation and improved Hough Transform algorithm which first defines the region of interest from input image for reducing searching space, divided the image into near field of view and far field of view. In near field of view, Hough transform has been applied to detect lane markers after image noise filtering. The proposed method has been developed using image processing programming language platform and was tested on collected video data. Promising result was obtained with high efficiency of detection.

53 citations


Additional excerpts

  • ...[2]....

    [...]

01 Jan 2009
TL;DR: A novel technique for eye detection using color and morphological image processing that is found to be highly efficient and accurate for detecting eyes in frontal face images.
Abstract: Eye detection is required in many applications like eye-gaze tracking, iris detection, video conferencing, auto-stereoscopic displays, face detection and face recognition. This paper proposes a novel technique for eye detection using color and morphological image processing. It is observed that eye regions in an image are characterized by low illumination, high density edges and high contrast as compared to other parts of the face. The method proposed is based on assumption that a frontal face image (full frontal) is available. Firstly, the skin region is detected using a color based training algorithm and six-sigma technique operated on RGB, HSV and NTSC scales. Further analysis involves morphological processing using boundary region detection and detection of light source reflection by an eye, commonly known as an eye dot. This gives a finite number of eye candidates from which noise is subsequently removed. This technique is found to be highly efficient and accurate for detecting eyes in frontal face images.

48 citations


"Safe driving by detecting lane disc..." refers methods in this paper

  • ...The commonly used approaches for passive eye detection include the template matching method, Eigen space method, and Hough transform-based method [5] [6], [4]....

    [...]

Proceedings ArticleDOI
18 Nov 2011
TL;DR: Non-intrusive technique is used in which no sensors are used on vehicle part as well as on body of the driver which was used in intrusive technique cause irritation in long time driving.
Abstract: The fatigue state of the driver is one of the important factors that cause traffic accidents. Vision based facial expression recognization technique is the most prospective method to detect driver fatigue. Therefore, a system that can detect oncoming driver drowsiness and issue timely warning could help in preventing many accidents and consequently save money and reduce personal suffering. By mounting a small camera inside the car the face of driver can be monitored. Firstly the face is detected by using skin color algorithm and then eyes are detected by using Circular Hough transform. This paper describes a method to track the eyes and detect whether the eyes are closed or open. If the eyes are found closed for 8 consecutive frames, the system draws the conclusion that the driver is falling asleep and issues a warning signal. In this paper non-intrusive technique is used in which no sensors are used on vehicle part as well as on body of the driver which was used in intrusive technique cause irritation in long time driving. The designed system is working properly in diverse conditions such as changes in light, shadow, and slightly dark background.

16 citations