scholarly journals Heavy Eyed Driver Detection System

2019 ◽  
Vol 9 (1) ◽  
pp. 666-669
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Great Element ◽  
Driver Fatigue ◽  
Car Accidents ◽  
Web Camera ◽  
The Face

Driver fatigue is a great element in a massive variety of car accidents. Drowsy driver warning gadgets can form the foundation of the machine to maybe minimize the accidents associated with drowsiness. This paper uses a web cam for picture capturing. A web camera is connected to PC, and images are received and processed with the aid of mat lab using image processing. By setting the camera interior the car, we can monitor the face of the driver and look for the eye actions that say whether the driver is in a situation to drive. If the gadget detects that the driver is drowsy, a warning alert is issued. If eyes are detected as shut for too lengthy a beep sound is produced and as a result alerting the driver.

scholarly journals Embedded Smart Car Security System on Face Detection

2014 ◽  
pp. 176-180
Author(s):  
Vikram Kulkarni ◽  
Viswaprakash Babu
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Security System ◽  
Experimental Results ◽  
Embedded Platform ◽  
Web Camera ◽  
The Face ◽  
Car Security ◽  
System Prototype

In this proposed embedded car security system, FDS(Face Detection System) is used to detect the face of the driver and compare it with the predefined face. For example, in the night when the car’s owner is sleeping and someone theft the car then FDS obtains images by one tiny web camera which can be hidden easily in somewhere in the car. FDS compares the obtained image with the predefined images if the image doesn’t match, then the information is sent to the owner through MMS. So now owner can obtain the image of the thief in his mobile as well as he can trace the location through GPS. The location of the car as well as its speed can be displayed to the owner through SMS. So by using this system, owner can identify the thief image as well as the location of the car This system prototype is built on the base of one embedded platform in which one SoC named “SEP4020”(works at 100MHz) controls all the processes .Experimental results illuminate the validity of this car security system.

scholarly journals Eye Detection System Based on Image Processing for Vehicle Safety

2020 ◽  
Vol 19 (01) ◽  
pp. 11-22
Author(s):  
Almira Budiyanto ◽  
Abdul Manan ◽  
Elvira Sukma Wahyuni
Keyword(s):  
Image Processing ◽  
Light Intensity ◽  
Digital Image Processing ◽  
Traffic Accidents ◽  
Detection System ◽  
High Accuracy ◽  
Vehicle Safety ◽  
Eye Detection ◽  
Eye Blinks ◽  
The Face

The more advanced the technology and the greater the community's need to carry out activities every day, the number of vehicles on the highway is getting crowded. From year to year, the greater the level of traffic accidents caused by many factors, among the usual reasons is the loss of awareness of the driver when driving a vehicle especially drowsiness. One of the drowsiness parameters is the frequency eye blinks. Therefore, to get the drowsiness symptoms, the purpose of this research is to detect the eye blinks, which in turn reduce the level of accidents by detecting sleepy eyes based on digital image processing. The method used to detect both eyes is the Viola-Jones method. The detection of both eyes can also acquire the duration of closed eyes and the number of eye blinks. A person can be said to be sleepy by means of sleepiness parameters determined by a study. The research shows that detection of eye blinks using the Viola-Jones method has a fairly high accuracy of up to 84.72% if the face condition is upright and tilted no more than 45 degrees. Another conclusion is that eye detection and driver detection are more effective at certain light intensity values which are around 2-33 lux.

Attendance Recording System using Face Recognition

2021 ◽  
Vol 9 (VII) ◽  
pp. 432-434
Author(s):  
Chandan R
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Data Base ◽  
Face Detection ◽  
Recording System ◽  
Reference Image ◽  
Web Camera ◽  
The Face ◽  
System Time ◽  
The Web

Image processing automated attendance system is the system in which easiest way to record the attendance for organization .This system is based on the face detection and face recognition algorithms. For this we make use of “Image Processing” using “MATLAB”. The concept of this paper is to provide real time attendance of students in a class to the faculty’s data base. Automatically detects the student using the web camera and only detect the facial part of that particular image and the image undergoes the various techniques and will compare with reference image, Later the attendance of the student is updated .Thus with the help of this system time will be saved and it is so convenient to record the attendance at any time throughout the day.

scholarly journals Drowsiness Detection System with Speed Limit Recommendation using Sentiment Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 184-190
Author(s):  
Kavyashree Subramonian* ◽  
Sumathi, G.
Keyword(s):  
Detection System ◽  
Health Hazard ◽  
The Body ◽  
Speed Limit ◽  
Tracking Accuracy ◽  
Essential Information ◽  
Drowsiness Detection ◽  
Car Accidents ◽  
State Of Mind ◽  
The Face

Driving while drowsy is a ubiquitous and extremely grave public health hazard that requires immediate consideration. Through studies in recent years, it has been proved that about 20 percent of all car accidents have occurred as a result of dozy driving. The main objective of new drowsiness detection systems is accurate doziness recognition. In this regard, the face is the most important part of the body as it sends a lot of essential information. The facial expressions of a drowsy driver include frequency of blinking and yawning. This paper proposes a model which detects the drivers' awareness using video stills of the driver’s face and improves the tracking accuracy. Further, we introduce the auxiliary functionality of speed limit recommendations based on the driver’s present state of mind. The various facial features are evaluated to determine the drivers' current state. By combining the features of the eyes and mouth, the driver is alerted with a fatigue warning and also suggested a safe speed limit. This system is very essential so as to prevent and hence reduce the number of fatal accidents that occur as a result of dozy driving saving a lot of lives and damage to property.

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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