Fatigue Detection System Based on Kalman Filtering and Dynamic Template

2013 ◽  
Vol 380-384 ◽  
pp. 3921-3924
Author(s):  
Lan Shi ◽  
Yi Hong Zhang
Keyword(s):  
Detailed Analysis ◽  
Face Detection ◽  
Kalman Filtering ◽  
Detection Rate ◽  
Traffic Accidents ◽  
Detection System ◽  
Eye Detection ◽  
New Approach ◽  
Dynamic Template ◽  
Fatigue Detection

In order to reduce the incidence of traffic accidents, it did detailed analysis and further research on fatigue detection system.this system include face detection, eye detection, eye tracking. During the eye detection, it proposed a new approach based on Kalman filtering and dynamic template. And then it did experiments on the detection rate, the PERCLOSs numerical value and the speed. Experiment results show that the detection results can meet the demand of practice. It turns out that this system can meet the demand of basic practice, has an extensive application field.

scholarly journals In-tunnel Accident Detection System based on the Learning of Accident Sound

2021 ◽  
Vol 15 (1) ◽  
pp. 81-92
Author(s):  
Linyang Yan ◽  
Sun-Woo Ko
Keyword(s):  
Neural Network ◽  
Detection Rate ◽  
Traffic Accidents ◽  
Deep Neural Network ◽  
Detection System ◽  
Recall Rate ◽  
Vehicle Accident ◽  
Special Environment ◽  
Accident Detection ◽  
Sound Features

Introduction: Traffic accidents are easy to occur in the tunnel due to its special environment, and the consequences are very serious. The existing vehicle accident detection system and CCTV system have the issues of low detection rate. Methods: A method of using Mel Frequency Cepstrum Coefficient (MFCC) to extract sound features and using a deep neural network (DNN) to learn sound features is proposed to distinguish accident sound from the non-accident sound. Results and Discussion: The experimental results show that the method can effectively classify accident sound and non-accident sound, and the recall rate can reach more than 78% by setting appropriate neural network parameters. Conclusion: The method proposed in this research can be used to detect tunnel accidents and consequently, accidents can be detected in time and avoid greater disasters.

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.

scholarly journals An Automatic Face Detection System for RGB Images

2011 ◽  
Vol 6 (1) ◽  
pp. 21 ◽  
Author(s):  
Tudor Barbu
Keyword(s):  
Face Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Detection System ◽  
Color Images ◽  
Human Face ◽  
Skin Region ◽  
Detection Approach ◽  
Rgb Images ◽  
Digital Color Images

We propose a robust face detection approach that works for digital color images. Our automatic detection method is based on image skin regions, therefore a skin-based segmentation of RGB images is provided first. Then, we decide for each skin region if it represents a human face or not, using a set of candidate criteria, an edge detection process, a correlation based technique and a threshold-based method. A high face detection rate is obtained using the proposed method.

Driver Fatigue Detection Based On Facial Feature Analysis

2021 ◽  
pp. 2150034
Author(s):  
Yimin Zhang ◽  
Xianwei Han ◽  
Wei Gao ◽  
Yunliang Hu
Keyword(s):  
Traffic Accidents ◽  
Detection System ◽  
Recognition Rate ◽  
Ccd Camera ◽  
Facial Feature ◽  
The State ◽  
Driver Fatigue ◽  
Eyelid Closure ◽  
Fatigue Driving ◽  
Fatigue Detection

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

scholarly journals Driver’s Fatigue Detection Based on Yawning Extraction

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Nawal Alioua ◽  
Aouatif Amine ◽  
Mohammed Rziza
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Training Data ◽  
Video Sequences ◽  
Support Vector ◽  
New Approach ◽  
Lighting Conditions ◽  
Web Camera ◽  
Real Video ◽  
Fatigue Detection

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.

scholarly journals Fatigue Detection Using Computer Vision

2010 ◽  
Vol 56 (4) ◽  
pp. 457-461 ◽  
Author(s):  
Mitesh Patel ◽  
Sara Lal ◽  
Diarmuid Kavanagh ◽  
Peter Rossiter
Keyword(s):  
Computer Vision ◽  
Detection System ◽  
Face Image ◽  
Blink Rate ◽  
Eye Detection ◽  
Skin Segmentation ◽  
Video Images ◽  
Long Duration ◽  
The Face ◽  
Fatigue Detection

Fatigue Detection Using Computer VisionLong duration driving is a significant cause of fatigue related accidents of cars, airplanes, trains and other means of transport. This paper presents a design of a detection system which can be used to detect fatigue in drivers. The system is based on computer vision with main focus on eye blink rate. We propose an algorithm for eye detection that is conducted through a process of extracting the face image from the video image followed by evaluating the eye region and then eventually detecting the iris of the eye using the binary image. The advantage of this system is that the algorithm works without any constraint of the background as the face is detected using a skin segmentation technique. The detection performance of this system was tested using video images which were recorded under laboratory conditions. The applicability of the system is discussed in light of fatigue detection for drivers.

LSO-AdaBoost Based Face Detection for IP-CAM Video

2013 ◽  
Vol 284-287 ◽  
pp. 3543-3548 ◽  
Author(s):  
Chuang Jan Chang ◽  
Shu Lin Hwang
Keyword(s):  
Video Surveillance ◽  
Face Detection ◽  
Motion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Training Data ◽  
Surveillance Systems ◽  
High Detection Rate ◽  
Data Set ◽  
Adaboost Algorithm

The IP-CAM plays a major role in the context of digital video surveillance systems. The function of face detection can add extra value and can contribute towards an intelligent video surveillance system. The cascaded AdaBoost-based face detection system proposed by Viola can support real-time detection with a high detection rate. The performance of the Alt2 cascade (from OpenCV) in an IP-CAM video is worse than that with regard to static images because the training data set in the Alt2 cannot consider the localized characters in the special IP-CAM video. Therefore, this study presents an enhanced training method using the Adaboost algorithm which is capable of obtaining the localized sampling optimum (LSO) from a local IP-CAM video. In addition, we use an improved motion detection algorithm that cooperates with the former face detector to speed up processing time and achieve a better detection rate on video-rate processing speed. The proposed solution has been developed around the cascaded AdaBoost approach, using the open-CV library, with a LSO from a local IP-CAM video. An efficient motion detection model is adopted for practical applications. The overall system performance using 30% local samples can be improved to a 97.9% detection rate and reduce detection time by 54.5% with regard to the Alt2 cascade.

scholarly journals Development of Driver Fatigue Detection System By Using Video Images

2019 ◽  
pp. 26-29
Author(s):  
Burcu Kir Savas ◽  
Yasar Becerikli
Keyword(s):  
Principle Component Analysis ◽  
Traffic Accidents ◽  
Detection System ◽  
Adaboost Algorithm ◽  
Principle Component ◽  
Detection And Tracking ◽  
Video Images ◽  
The World ◽  
Eye Closure ◽  
Fatigue Detection

Major reasons for traffic accidents all over the world are mostly because of drivers' fatigue and lack of concentration. In this study, the detection and tracking of the drivers' faces in video based images were realized by using AdaBoost algorithm. The eye area was detected by using Principle Component Analysis (PCA). A predictive system was developed analyzing the eye closure of the drivers'. The system used PERCLOS (Percentage of eye closure) and it was tested on UCLA database.

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