SUBJECTIVE METHODS FOR ASSESSMENT OF DRIVER DROWSINESS

The paper deals with the issue of fatigue and sleepiness behind the wheel, which for a long time has been of vital importance for the research in the area of driver-car interaction safety. Numerous experiments on car simulators with diverse measurements to observe human behavior have been performed at the laboratories of the faculty of the authors. The paper provides analysis and an overview and assessment of the subjective (self-rating and observer rating) methods for observation of driver behavior and the detection of critical behavior in sleep deprived drivers using the developed subjective rating scales

Image Processing for Rapidly Eye Detection based on Robust Haar Sliding Window

Object Detection using Haar Cascade Clasifier widely applied in several devices and applications as a medium of interaction between human and computer such as a tool control that utilizes the detection of eye movements. Obviously speed and precision in the detection process such as eyes, has an effect if implemented on a device. If the eye could not detect accurately, controlling device systems could reach bad detection as well. The proposed method can be used as an approach to detect the eye region of eye based on haar classifier method by means of modifying the direction of sliding window. In which, it was initially placed in the middle position of image on facial area by assuming the location of eyes area in the central region of the image. While the window region of conventional haar cascade scan the whole of image start from the left top corner. From the experiment by using our proposed method, it can speed up the the computation time and improve accuracy significantly reach to 92,4%

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

Efficient and Robust Driver Fatigue Detection Framework Based on the Visual Analysis of Eye States

Fatigue detection based on vision is widely employed in vehicles due to its real-time and reliable detection results. With the coronavirus disease (COVID-19) outbreak, many proposed detection systems based on facial characteristics would be unreliable due to the face covering with the mask. In this paper, we propose a robust visual-based fatigue detection system for monitoring drivers, which is robust regarding the coverings of masks, changing illumination and head movement of drivers. Our system has three main modules: face key point alignment, fatigue feature extraction and fatigue measurement based on fused features. The innovative core techniques are described as follows: (1) a robust key point alignment algorithm by fusing global face information and regional eye information, (2) dynamic threshold methods to extract fatigue characteristics and (3) a stable fatigue measurement based on fusing percentage of eyelid closure (PERCLOS) and proportion of long closure duration blink (PLCDB). The excellent performance of our proposed algorithm and methods are verified in experiments. The experimental results show that our key point alignment algorithm is robust to different scenes, and the performance of our proposed fatigue measurement is more reliable due to the fusion of PERCLOS and PLCDB

Human Automotive Interaction: Affect Recognition for Motor Trend Magazine\u27s Best Driver Car of the Year

Observation analysis of vehicle operators has the potential to address the growing trend of motor vehicle accidents. Methods are needed to automatically detect heavy cognitive load and distraction to warn drivers in poor psychophysiological state. Existing methods to monitor a driver have included prediction from steering behavior, smart phone warning systems, gaze detection, and electroencephalogram. We build upon these approaches by detecting cues that indicate inattention and stress from video. The system is tested and developed on data from Motor Trend Magazine\u27s Best Driver Car of the Year 2014 and 2015. It was found that face detection and facial feature encoding posed the most difficult challenges to automatic facial emotion recognition in practice. The chapter focuses on two important parts of the facial emotion recognition pipeline: (1) face detection and (2) facial appearance features. We propose a face detector that unifies state‐of‐the‐art approaches and provides quality control for face detection results, called reference‐based face detection. We also propose a novel method for facial feature extraction that compactly encodes the spatiotemporal behavior of the face and removes background texture, called local anisotropic‐inhibited binary patterns in three orthogonal planes. Real‐world results show promise for the automatic observation of driver inattention and stress

A Comparative Emotions-detection Review for Non-intrusive Vision-Based Facial Expression Recognition

Affective computing advocates for the development of systems and devices that can recognize, interpret, process, and simulate human emotion. In computing, the field seeks to enhance the user experience by finding less intrusive automated solutions. However, initiatives in this area focus on solitary emotions that limit the scalability of the approaches. Further reviews conducted in this area have also focused on solitary emotions, presenting challenges to future researchers when adopting these recommendations. This review aims at highlighting gaps in the application areas of Facial Expression Recognition Techniques by conducting a comparative analysis of various emotion detection datasets, algorithms, and results provided in existing studies. The systematic review adopted the PRISMA model and analyzed eighty-three publications. Findings from the review show that different emotions call for different Facial Expression Recognition techniques, which should be analyzed when conducting Facial Expression Recognition. Keywords: Facial Expression Recognition, Emotion Detection, Image Processing, Computer Visio

Drowsiness monitoring system using artificial intelligent technique

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Drowsiness Monitoring System Using Artificial lntelligent Technique.

ได้รับทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนาร