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Vision based system for driver drowsiness detection

https://doi.org/10.1109/isps.2013.6581501
0 signed · 21 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/c2009-0-25049-5openalex-snapshot
Feature Extraction and Image Processing
inferredcites →https://doi.org/10.1109/aina.2010.151openalex-snapshot
Bright Pupil Detection in an Embedded, Real-Time Drowsiness Monitoring System
inferredcites →https://doi.org/10.1109/cvpr.2001.990517openalex-snapshot
Rapid object detection using a boosted cascade of simple features
inferredcites →https://doi.org/10.1109/icca.2007.4376601openalex-snapshot
An Improved Real Time Eye State Identification System in Driver Drowsiness Detection
inferredcites →https://doi.org/10.1109/icip.2002.1038171openalex-snapshot
An extended set of Haar-like features for rapid object detection
inferredcites →https://doi.org/10.1109/icsipa.2009.5478674openalex-snapshot
Detection and classification of eye state in IR camera for driver drowsiness identification
inferredcites →https://doi.org/10.1109/icves.2005.1563658openalex-snapshot
Real-time driver's eye state detection
inferredcites →https://doi.org/10.1109/iembs.2010.5626013openalex-snapshot
Automated video-based measurement of eye closure for detecting behavioral microsleep
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4183382openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-031-45121-8_5openalex-snapshot
Real-Time Drowsiness Detection System Using Machine Learning
inferred← citeshttps://doi.org/10.1007/978-981-16-2406-3_27openalex-snapshot
Computer Vision Based Driver Assistance Drowsiness Detection
inferred← citeshttps://doi.org/10.1007/978-981-19-0312-0_34openalex-snapshot
Mobile Application on Drowsiness Detection When Driving Car
inferred← citeshttps://doi.org/10.1016/j.micpro.2020.103275openalex-snapshot
Smart driver assistance system using raspberry pi and sensor networks
inferred← citeshttps://doi.org/10.1063/5.0198486openalex-snapshot
An early warning system of fatigue detection of drivers using novel image processing and HOG for evaluation of the driver quality using Viola-Jones over hog algorithm
inferred← citeshttps://doi.org/10.1109/icaiccit60255.2023.10465952openalex-snapshot
Driver’s Drowsiness Detection System Using Machine Learning
inferred← citeshttps://doi.org/10.1109/iccebs58601.2023.10448968openalex-snapshot
An Early Warning System for Driver Fatigue Detection Using Viola-Jones Over Hog Algorithm
inferred← citeshttps://doi.org/10.1109/icesc65114.2025.11212441openalex-snapshot
Enhancing Road Safety Detection System using Visual Fatigue Indicators
inferred← citeshttps://doi.org/10.1109/iciccs51141.2021.9432361openalex-snapshot
Driver Assistance System using Raspberry Pi and Haar Cascade Classifiers
inferred← citeshttps://doi.org/10.3390/engproc2023046039openalex-snapshot
Enhancing Driver Safety: Real-Time Eye Detection for Drowsiness Prevention Driver Assistance Systems
inferred← citeshttps://doi.org/10.3390/s23010263openalex-snapshot
Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors
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