scholarly journals Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2908
Author(s):  
Yi Wang ◽  
Zhengxiang He ◽  
Liguan Wang
Keyword(s):  
Face Detection ◽  
Classification Model ◽  
Open Pit ◽  
Detection Methods ◽  
Driver Fatigue ◽  
Tracking Method ◽  
Weak Space ◽  
Detection Stage ◽  
The Face ◽  
Fatigue Detection

Due to complex background interference and weak space–time connection, traditional driver fatigue detection methods perform poorly for open-pit truck drivers. For these issues, this paper presents a driver fatigue detection method based on Libfacedetection and an LRCN. The method consists of three stages: (1) using a face detection module with a tracking method to quickly extract the ROI of the face; (2) extracting and coding the features; (3) combining the coding model to build a spatiotemporal classification network. The innovation of the method is to utilize the spatiotemporal features of the image sequence to build a spatiotemporal classification model suitable for this task. Meanwhile, a tracking method is added to the face detection stage to reduce time expenditure. As a result, the average speed with the tracking method for face detection on video is increased by 74% in comparison with the one without the tracking method. Our best model adopts a DHLSTM and feature-level frame aggregation, which achieves high accuracy of 99.30% on the self-built dataset.

scholarly journals Implementation of face and eye detection on DM6437 board using simulink model

2020 ◽  
Vol 9 (2) ◽  
pp. 785-791
Author(s):  
B. Vijayalaxmi ◽  
Kaushik Sekaran ◽  
N. Neelima ◽  
P. Chandana ◽  
Maytham N. Meqdad ◽  
...  
Keyword(s):  
Research Work ◽  
Warning Signal ◽  
Blink Rate ◽  
Driver Fatigue ◽  
Simulink Model ◽  
Detection Stage ◽  
The Face ◽  
Fatigue Estimation ◽  
Motivating Factor ◽  
Fatigue Detection

Driver Assistance system is significant in drriver drowsiness to avoid on road accidents.  The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.

Driver fatigue detection based on convolutional neural network and face alignment for edge computing device

2021 ◽  
pp. 095440702199948
Author(s):  
Xiaofeng Li ◽  
Jiahao Xia ◽  
Libo Cao ◽  
Guanjun Zhang ◽  
Xiexing Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Edge Computing ◽  
Face Alignment ◽  
Driver Fatigue ◽  
Computing Device ◽  
The Face ◽  
Yaw Angle ◽  
Fatigue Detection

Most current vision-based fatigue detection methods don’t have high-performance and robust face detector. They detect driver fatigue using single detection feature and cannot achieve real-time efficiency on edge computing devices. Aimed at solving these problems, this paper proposes a driver fatigue detection system based on convolutional neural network that can run in real-time on edge computing devices. The system firstly uses the proposed face detection network LittleFace to locate the face and classify the face into two states: small yaw angle state “normal” and large yaw angle state “distract.” Secondly, the speed-optimized SDM algorithm is conducted only in the face region of the “normal” state to deal with the problem that the face alignment accuracy decreases at large angle profile, and the “distract” state is used to detect driver distraction. Finally, feature parameters EAR, MAR and head pitch angle are calculated from the obtained landmarks and used to detect driver fatigue respectively. Comprehensive experiments are conducted to evaluate the proposed system and the results show its practicality and superiority. Our face detection network LittleFace can achieve 88.53% mAP on AFLW test set at 58 FPS on the edge computing device Nvidia Jetson Nano. Evaluation results on YawDD, 300 W, and DriverEyes show the average detection accuracy of the proposed system can reach 89.55%.

scholarly journals Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

2019 ◽  
Vol 9 (20) ◽  
pp. 4344 ◽  
Author(s):  
Yang ◽  
Li ◽  
Min ◽  
Wang
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Region Of Interest ◽  
Detection Methods ◽  
Time Performance ◽  
Face Area ◽  
The Face ◽  
Two Stages ◽  
Coarse To Fine ◽  
Identification Mechanism

Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.

Face Tracking

2011 ◽  
pp. 92-162
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Image Sequence ◽  
Face Tracking ◽  
Detection Methods ◽  
Continuous Change ◽  
Image Frame ◽  
The Face ◽  
Previous Image

When we want to analyze the continuous change of the face in an image sequence, applying face tracking methods is a better choice than applying the face detection methods to each image frame. Usually, the face tracking methods are more efficient than the ordinary face detection methods because they can utilize the trajectory of the face in the previous image frames with an assumption that the shape, texture, or motion of the face change smoothly. There have been many approaches to face tracking. We divide the face tracking methods into several categories according to the cues that are extracted for tracking.

Driver Fatigue Detection Algorithm Research Based on the Characteristics of Eyes

2014 ◽  
Vol 701-702 ◽  
pp. 30-35 ◽  
Author(s):  
Qin Wang ◽  
Lan Tang ◽  
Kun Yang
Keyword(s):  
Warning System ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Driver Fatigue ◽  
Infrared Video ◽  
The Face ◽  
Eyes Open ◽  
Texture Detection ◽  
Opening And Closing ◽  
Fatigue Detection

In driver fatigue warning system, it is a very effective method for detecting Driver fatigue state through the driver's facial expressions and body movements. The main content of this article is to detect the two basic states of the eyes opening and closing and presents the LBP texture detection operator. Firstly we get the face image sequences using infrared video and extract the eye region using ADABOOST. The SVM is used in classifying feature vector of the eyes open and closed detecting of driver fatigue. A large number of experimental results show that the proposed method has high detection accuracy and timeliness.

Review of Techniques for Driver Fatigue Detection

2013 ◽  
Vol 433-435 ◽  
pp. 928-931 ◽  
Author(s):  
Jian Yong Zhang ◽  
Wen Wei Qiu ◽  
Hui Jian Fu ◽  
Meng Ting Zhang ◽  
Qing Guo Ma
Keyword(s):  
Detection Methods ◽  
Driving Safety ◽  
Significant Issue ◽  
Driver Fatigue ◽  
Detection Techniques ◽  
Behavioral Features ◽  
Fatigue Detection ◽  
Movement Tracking ◽  
Vehicle Movement ◽  
Broad Overview

Driver fatigue is a significant issue in driving safety. Large bodies of literature have examined fatigue detection methods and techniques. This paper provides a broad overview of the researches concerning related topics. So far, driver fatigue detection techniques developed can be divided into three categories, including techniques based on vehicle movement tracking, drivers behavioral features and electroencephalograph signals. In addition, a contrast between these techniques was presented and further researches were discussed.

Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images

2015 ◽  
Vol 5 (2) ◽  
pp. 35-57 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Human Skin ◽  
Face Detection ◽  
Skin Color ◽  
Color Images ◽  
Detection Methods ◽  
Skin Segmentation ◽  
The Face ◽  
Different Color ◽  
Lip Detection

Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.

scholarly journals Exploration of Face Detection Methods in Digital Images

2019 ◽  
Vol 8 (4) ◽  
pp. 12130-12136
Keyword(s):  
Face Detection ◽  
Digital Images ◽  
Recognition System ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Human Beings ◽  
Survey Paper ◽  
Wide Range ◽  
The Face

Face detection is a challenging computer vision task that identifies and localizes the faces of human beings from digital images or video streams. It is predominantly the first phase in the process of developing a wide range of face applications such as face recognition, emotion recognition, authentication, surveillance systems etc. The process of face detection is easy from the human perspective but, a complex task for computers that involves searching of the face in variable circumstances of pose, colour, size, occlusion, illumination etc. If the outcome of face detection is intended to be input for another algorithm, an accurate, well informed selection of an appropriate face detection technique is essential because the overall performance of face application is dependent on face detection algorithm’s precision. The survey paper presents a review of three commonly used face detection algorithms available in literature namely Viola Jones, Neural networks (NN) and Local Binary Pattern (LBP) for the purpose of ascertaining the most suitable face detection algorithm to implement for our future work in developing an ‘Online student concentration level recognition system’.

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