Fatigue features based on eye tracking for driver inattention system

Horák, Karel

doi:10.1109/tsp.2011.6043660

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Various studies have addressed the detection and tracking of facial landmarks including the iris and pupil which has various applications-particularly, eye gaze estimation for human-machine interfaces. The control of assistive devices for disability [2], driver safety improvements [3,4], the design of diagnostic tools for brain diseases [5], cognitive research [6], automated deception detection system (ADDS) [7], and academic performance analysis [8] are some examples of such applications.…”

Section: Introductionmentioning

confidence: 99%

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Khan

Hussain

Kuru

et al. 2020

Sensors

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Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection.

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Section: Introductionmentioning

confidence: 99%

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Khan

Hussain

Kuru

et al. 2020

Sensors

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Cloud based highways safety system through real time monitoring of driver sleepiness condition

Vijay

Krishna

Thazeem

et al. 2022

Eighth International Conference New Trends in the Applications of Differential Equations in Sciences (Ntades2021)

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Weak Classifier for Density Estimation in Eye Localization and Tracking

Araujo

Ribeiro

Waldir

et al. 2017

IEEE Trans. on Image Process.

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In this paper, we propose a fast weak classifier that can detect and track eyes in video sequences. The approach relies on a least-squares detector based on the inner product detector (IPD) that can stimate a probability density distribution for a feature's location-which fits naturally with a Bayesian estimation cycle, such as a Kalman or particle filter. As a least-squares sliding window detector, it possesses tolerance to small variations in the desired pattern while maintaining good generalization capabilities and computational efficiency. We propose two approaches to integrating the IPD with a particle filter tracker. We use the BioID, FERET, LFPW, and COFW public datasets as well as five manually annotated high-definition video sequences to quantitatively evaluate the algorithms' performance. The video data set contains four subjects, different types of backgrounds, blurring due to fast motion, and occlusions. All code and data are available.

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Fatigue features based on eye tracking for driver inattention system

Cited by 4 publications

References 11 publications

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Cloud based highways safety system through real time monitoring of driver sleepiness condition

Weak Classifier for Density Estimation in Eye Localization and Tracking

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