Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations and extracts the vertical distance between the eyelids using the facial landmark positions. Our results show that the recognizable landmarks are sufficiently accurate to determine the degree of eye-opening and closing consistently. The proposed algorithm estimates the facial landmark positions, extracts a single scalar quantity by using Modified Eye Aspect Ratio (Modified EAR) and characterizing the eye closeness in each frame. Finally, blinks are detected by the Modified EAR threshold value and detecting eye blinks as a pattern of EAR values in a short temporal window. According to the results from a typical data set, it is seen that the suggested approach is more efficient than the state-of-the-art technique.

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

This paper presents an adaptive camera-based eye blink detection algorithm for assessing the level of drowsiness during driving. The data used in this study were collected from driving simulator experiments using a remote camera. Eye blink detection in the automotive context and for different driver states typically encounters some difficulties. It may be challenging to reliably distinguish between eye blink events and gaze-related eyelid closures, particularly the glances at the dashboard, since both exhibit a similar eyelid movement pattern. In addition, it is difficult to find comparable thresholds due to high inter-individual differences in the palpebral aperture. Furthermore, the blinking behavior is impacted by drowsiness and the blink patterns vary widely, which requires an adaptive algorithm to deal with this intra-individual variability of the blinks. These challenges are considered in the design of the presented blink detection algorithm. This algorithm is based essentially on a threshold for the maximum velocity of the eyelids. This threshold is determined using k-means clustering (k=2)and updated every five minutes of the drive. The accuracy of the algorithm is evaluated based on video labeling. The detection rates demonstrate that the algorithm performs very reliably in both awake and drowsy phases of the driving experiments.

Camera-Based Eye Blink Detection Algorithm for Assessing Driver Drowsiness.

Driver drowsiness and distraction are now widely recognized as important contributors to deadly road accidents around the world. This typically occurs when a driver has not slept enough, but it can also occur as a result of untreated sleep problems, drugs, alcohol consumption, or shift employment. As a result, driver monitoring and identification are becoming increasingly important features of car safety systems. Head position, gaze direction, yawning, and eye state analysis are among the essential aspects. This research proposes a driver drowsiness detection system that uses eye blink, mouth opening and closing counts to detect drowsiness. When the driver's eyes are closed for an extended period of time, an alert sound is generated to notify him. Furthermore, the vehicle's owner is notified by e-mail if the driver is observed to be napping off more than a few times, in order to certify that the driver is taking certain steps to avoid falling asleep. The output of the system proposed in the paper on Deep learning technology of Dlib which uses CNN (Convolutional Neural Network) as its base algorithm for accurate detection, OpenCV, and Raspberry Pi environments with a mounted camera for the same, show that system achieves good result when it comes to drowsiness detection, reducing the overall number of accidents on the streets. For Realtime video input, the proposed method had a 96% of accuracy.

Deep Learning based Driver Drowsiness Detection

Drowsiness is always a major concern that requires a high level of concentration in driving, piloting, controlling air traffic and industry labor. Yet, drowsiness drivers are the cause of many road accidents. State detection of driver drowsiness at runtime is a big challenge. Drowsiness not only decreases attention level but also slows down the retort time. A number of techniques have been suggested by researchers to detect driver drowsiness. Most of them are based on machine learning, deep learning, or processing the image of the face of the driver by extracting the facial expressions. This paper explores all the existing techniques used by researchers based on different features followed by the discussion over the result on driver drowsiness. The paper also focused on the comparative analysis of the techniques and suggested the best one. Almost all the techniques have common recommendation that the embedded software-based device is necessary to be mounted on the vehicle which can alerts the driver by early predicting the sleepiness to awake him. The aim of this paper is to bring together the current methods to resolve this issue. Moreover, the paper explored challenges that still exist in the current era. Finally, the paper is concluded the future aspects that can work in real-time scenario specially is night vision.

Significance, Techniques, and Challenges in Driver Drowsiness Issue: A Guide Tour

Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.

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EEG channel selection based on sequential backward floating search for motor imagery classification

Modern advanced driver-assistance systems analyze the driving performance to gather information about the driver's state. Such systems are able, for example, to detect signs of drowsiness by evaluating the steering or lane keeping behavior and to alert the driver when the drowsiness state reaches a critical level. However, these kinds of systems have no access to direct cues about the driver's state. Hence, the aim of this work is to extend the driver drowsiness detection in vehicles using signals of a driver monitoring camera. For this purpose, 35 features related to the driver's eye blinking behavior and head movements are extracted in driving simulator experiments. Based on that large dataset, we developed and evaluated a feature selection method based on the k-Nearest Neighbor algorithm for the driver's state classification. A concluding analysis of the best performing feature sets yields valuable insights about the influence of drowsiness on the driver's blink behavior and head movements. These findings will help in the future development of robust and reliable driver drowsiness monitoring systems to prevent fatigue-induced accidents.

Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm

Scalable systems for automated driving have to reliably cope with an open-world setting. This means, the perception systems are exposed to drastic domain shifts, like changes in weather conditions, time-dependent aspects, or geographic regions. Covering all domains with annotated data is impossible because of the endless variations of domains and the time-consuming and expensive annotation process. Furthermore, fast development cycles of the system additionally introduce hardware changes, such as sensor types and vehicle setups, and the required knowledge transfer from simulation. To enable scalable automated driving, it is therefore crucial to address these domain shifts in a robust and efficient manner. Over the last years, a vast amount of different domain adaptation techniques evolved. There already exists a number of survey papers for domain adaptation on camera images, however, a survey for LiDAR perception is absent. Nevertheless, LiDAR is a vital sensor for automated driving that provides detailed 3D scans of the vehicle's surroundings. To stimulate future research, this paper presents a comprehensive review of recent progress in domain adaptation methods and formulates interesting research questions specifically targeted towards LiDAR perception.

Mariella Dreissig

Papers

Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm

A Survey on Deep Domain Adaptation for LiDAR Perception.