Yawning Detection Using Embedded Smart Cameras

Proceedings of the International Conference on Computer-Human Interaction Research and Applications, 2017

Nowadays drivers fatigue is amongst significant causes of traffic accidents. There exist many academic and industrial publications, where fatigue detection is presented. Yawning is one of the most detectable and indicative symptoms in such situation. However, yawning identification approaches which have been developed to date are limited by the fact that they detect a wide open mouth. And the detection of open mouth can also mean talking, singing and smiling, what is not always a sign of fatigue. The research aims was to investigate the different situations when the mouth is open and distinguish situation when really yawning occurred. In this paper we use an algorithm for localization of the facial landmarks and we propose a simple and effective system for yawning detection which is based on changes of mouth geometric features. The accuracy of presented method was verified using 80 videos collected from three databases: we have used 20 films of yawning expression, 30 films of smiling and 30 films with singing examples. The experimental results show high accuracy of proposed method on the level of 93%. The obtained results have been compared with the methods described in the literature-the achieved accuracy puts proposed method among the best solutions of recent years.

One of the most common issues over the globe these days is the blasting number of street mishaps. Inappropriate and preoccupied driving is one of the significant reason for street mishaps. Driver's sluggishness or absence of fixation is considered as a predominant purpose behind such incidents. Research in the field of driver languor observing may assist with lessening the mishaps. This paper thusly proposes a non-nosy approach for executing a driver's languor ready framework which would distinguish and screen the yawning and languor of the driver. The framework utilizes Histogram Oriented Gradient (HOG) highlight descriptor for face location and facial focuses acknowledgment. At that point SVM is utilized to check whether distinguished article is face or non-face. It further screens Mouth Aspect Ratio (MAR) of the driver up to a fixed number of casings to check the languor and yawning. Since the sluggishness or on the other hand tiredness of the driver is likewise founded on the quantity of hours the person in question has been driving, an extra component of shifting the edge outlines for mouth is incorporated. This makes the framework progressively delicate towards sleepiness identification.

Signal Processing, Image …, 2009

For the safety purpose of drivers yawning detection is very important. There are many important applications of yawning in a drivers fatigue detection, humans well behavior, driving behavior and understanding the language of tongue disability person. It is observed that heavy vehicle drivers keep going continuously driving without giving a frequent rest period. Considering above applications, an automatic detection of yawning is one important system component. Uptil now yawning detection system does not satisfy the real time requirement which is having a high computational complexity and does not satisfies the challenges like facial obstruction, ease of implementation, accuracy and safety. Uptil now there is no any idea suggested or presented on vehicle side hardware. In this paper we will be reviewing hardware of engine start or stop control by using microcontroller. In previous, for yawning detection two different algorithms are used such as for face viola-Jones and mouth detection contour activation algorithm. Previously presented systems are in position to recognize other persons faces while finding the biggest face in all of the frame that we are interested and other faces also considered; hence face search time is not optimized. By studying these disadvantages of traditional yawning detection method possible improvement algorithm is proposed.

2019

Detecting eye blink and yawning is important, for example in systems that monitor the vigilance of the human operator, eg Driver's drowsiness. Driver fatigue is one of the leading causes of the worlds deadliest road accidents. This shows that in the transport sector in particular, where a driver of heavy vehicles is often open to hours of monotonous driving which causes fatigue without frequent rest periods. It is therefore essential to design a road accident prevention system that can detect the drivers drowsiness, determine the drivers level of carelessness and warn when an imminent danger occurs. In this article, we propose a real time system that uses eye detection techniques, blinking and yawning. The system is designed as a non intrusive real time monitoring system. The priority is to improve driver safety without being intrusive. In this work, the blink of an eye and the drivers yawn are detected. If the drivers eyes remain closed for more than a certain time and the driv...

Biomedical Signal Processing and Control, 2015

One of the most common signs of tiredness or fatigue is yawning. Naturally, identification of fatigued individuals would be helped if yawning is detected. Existing techniques for yawn detection are centred on measuring the mouth opening. This approach, however, may fail if the mouth is occluded by the hand, as it is frequently the case. The work presented in this paper focuses on a technique to detect yawning whilst also allowing for cases of occlusion. For measuring the mouth opening, a new technique which applies adaptive colour region is introduced. For detecting yawning whilst the mouth is occluded, Local Binary Pattern (LBP) features are used to also identify facial distortions during yawning. In this research, the Strathclyde Facial Fatigue (SFF) database which contains genuine video footage of fatigued individuals is used for training, testing and evaluation of the system.

Driver fatigue is the main reason for fatal road accidents around the world. In this paper, an efficient driver's drowsiness detection system is designed using yawning detection.Here, we consider eye detection and mouth detection. So that road accidents can avoid successfully. Mouth features points are identified using the redness property. Firstly detecting the driver's face using YCbCr method then face tracking will perform using canny edge detector. After that , eyes and mouth positions by using Haar features. Lastly yawning detection is perform by using mouth geometric features. This method is tested on images from videos. Also proposed system should then alert to the driver in case of inattention.

IRJET, 2020

One of the most common issues over the globe these days is the blasting number of street mishaps. Inappropriate and preoccupied driving is one of the significant reason for street mishaps. Driver's sluggishness or absence of fixation is considered as a predominant purpose behind such incidents. Research in the field of driver languor observing may assist with lessening the mishaps. This paper thusly proposes a non-nosy approach for executing a driver's languor ready framework which would distinguish and screen the yawning and languor of the driver. The framework utilizes Histogram Oriented Gradient (HOG) highlight descriptor for face location and facial focuses acknowledgment. At that point SVM is utilized to check whether distinguished article is face or non-face. It further screens Mouth Aspect Ratio (MAR) of the driver up to a fixed number of casings to check the languor and yawning. Since the sluggishness or on the other hand tiredness of the driver is likewise founded on the quantity of hours the person in question has been driving, an extra component of shifting the edge outlines for mouth is incorporated. This makes the framework progressively delicate towards sleepiness identification.

International Journal of Vehicular Technology, 2014

The increasing number of traffic accidents is principally caused by fatigue. In fact, the fatigue presents a real danger on road since it reduces driver capacity to react and analyze information. In this paper we propose an efficient and nonintrusive system for monitoring driver fatigue using yawning extraction. The proposed scheme uses face extraction based support vector machine (SVM) and a new approach for mouth detection, based on circular Hough transform (CHT), applied on mouth extracted regions. Our system does not require any training data at any step or special cameras. Some experimental results showing system performance are reported. These experiments are applied over real video sequences acquired by low cost web camera and recorded in various lighting conditions.

2014

Now a days the driver drowsiness is leading cause for major accidents. The regular monitoring of drivers drowsiness is one of the best solution in order to reduce the accidents caused by drowsiness. In order to detect and remove this cause of road accident many driver fatigue detection methods have been proposed.Consequently, it is very necessary to design a road accidents prevention system by detecting driver’s drowsiness, which determines the level of driver inattentiveness and give a warning when an impending danger exists. In this paper, a simulation and analysis of fusion method has done. This method of eye blinking and yawning detection is based on the changes in the mouth geometric features. The programming for this is done in OpenCV using the Haarcascade library for the detection of facial features and Active Contour Method for the activity of lips . Keywords— Driver Face Detection, Driver Eye Blink Detection, Driver Yawning Detection, Driver Drowsiness, Real time system, RO...