Real-Time Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Detection

• URL: http://arxiv.org/abs/2408.05836v1
• Date: Sun, 11 Aug 2024 17:34:24 GMT
• Title: Real-Time Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Detection
• Authors: Varun Shiva Krishna Rupani, Velpooru Venkata Sai Thushar, Kondadi Tejith,
• Abstract summary: This study presents a real-time system designed to detect drowsiness using the Eye Aspect Ratio (EAR) and facial landmark detection techniques. By establishing a threshold for the EAR, the system identifies when eyes are closed, indicating potential drowsiness. Experiments show that the system reliably detects drowsiness with high accuracy while maintaining low computational demands.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drowsiness detection is essential for improving safety in areas such as transportation and workplace health. This study presents a real-time system designed to detect drowsiness using the Eye Aspect Ratio (EAR) and facial landmark detection techniques. The system leverages Dlibs pre-trained shape predictor model to accurately detect and monitor 68 facial landmarks, which are used to compute the EAR. By establishing a threshold for the EAR, the system identifies when eyes are closed, indicating potential drowsiness. The process involves capturing a live video stream, detecting faces in each frame, extracting eye landmarks, and calculating the EAR to assess alertness. Our experiments show that the system reliably detects drowsiness with high accuracy while maintaining low computational demands. This study offers a strong solution for real-time drowsiness detection, with promising applications in driver monitoring and workplace safety. Future research will investigate incorporating additional physiological and contextual data to further enhance detection accuracy and reliability.

Related papers

• Bed-Attached Vibration Sensor System: A Machine Learning Approach for Fall Detection in Nursing Homes [33.45861095003339]
  This study presents the development of an automated fall detection system integrated into care beds, aimed at enhancing patient safety without compromising privacy through wearables or video monitoring. Mechanical vibrations transmitted through the bed frame are processed using a short-time Fourier transform, enabling robust classification of distinct human fall patterns with a convolutional neural network. Despite limited available data, the proposed system shows the potential for an accurate and rapid response to falls, mitigating health implications, and addressing the needs of an aging population.
  arXiv  Detail & Related papers  (2024-12-06T11:08:47Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions [2.0409124291940826]
  This study proposes a novel deep learning framework inspired by atmospheric scattering and human visual cortex mechanisms to enhance object detection under poor visibility scenarios such as fog, smoke, and haze. The objective is to enhance the precision and reliability of detection systems under adverse environmental conditions.
  arXiv  Detail & Related papers  (2024-10-02T04:03:07Z)
• A Survey on Drowsiness Detection -- Modern Applications and Methods [29.367684013050916]
  Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel. This comprehensive review explores the significance of drowsiness detection in various areas of application.
  arXiv  Detail & Related papers  (2024-08-23T11:15:21Z)
• Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
  Engagement measurement finds application in healthcare, education, services. Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
  arXiv  Detail & Related papers  (2024-04-05T20:39:16Z)
• Detection Latencies of Anomaly Detectors: An Overlooked Perspective ? [1.8492669447784602]
  In this paper, we argue the relevance of measuring the temporal latency of attacks and errors. We propose an evaluation approach for detectors to ensure a pragmatic trade-off between correct and in-time detection.
  arXiv  Detail & Related papers  (2024-02-14T10:52:39Z)
• Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection [96.539862328788]
  Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. We propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF) Experiment results on several public face forgery datasets demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-21T08:58:49Z)
• Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery [1.4367226581254677]
  This work investigates the feasibility of using an OoD detector to identify when images from the ii OCT probe are inappropriate for machine learning-based distance estimation. We show how a simple OoD detector based on the Mahalanobis distance can successfully reject corrupted samples coming from real-world ex vivo porcine eyes.
  arXiv  Detail & Related papers  (2023-04-11T07:54:11Z)
• A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications [56.458448869572294]
  We introduce DETRtime, a novel framework for time-series segmentation of EEG data. Our end-to-end deep learning-based framework brings advances in Computer Vision to the forefront. Our model generalizes well in the task of EEG sleep stage segmentation.
  arXiv  Detail & Related papers  (2022-06-17T10:17:24Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.