Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance

By Qiang Ji, Xiaojie Yang. Real-time imaging, 2002

620 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Social Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/fd61a8d6ebd6109a63bf2c68fa19731495a573ac

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In Experimental and Cognitive Psychology

In Social Psychology

In Human-Computer Interaction

Citations in the corpus

Driver Modeling for Detection and Assessment of Driver Distraction: Examples from the UTDrive Test Bed (2017, background): “Understanding where the visual focus lies is a key step to determine driver performance during the primary driving task [49]–[51].”
Multimodal Human Computer Interaction: A Survey (2005, background): “Real-time vision for HCI (gestures, object tracking, hand posture, gaze, face pose) is discussed in [84] and [77].”
Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, methodology): “The ocular parameters such as ECD, PERCLOS, eye closure/opening speed, and blink frequency were calculated as functions of the FSM. Ji and Yang [105] used Kalman filtering to track eyelid movements and compute the PERCLOS and eye closure speed [average eye closure speed (AECS)].”
Differential entropy feature for EEG-based vigilance estimation (2013, background): “Up to now, various physiological measures are studied for vigilance analysis, such as eye movement [4] and autonomic nervous system activity.”
Combining Head Pose and Eye Location Information for Gaze Estimation (2012, background): “proposed by in [21] describe a real-time eye, gaze, and head pose tracker for monitoring driver vigilance.”
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze (2010, methodology): “Yang et al. and Stiefelhagen et al. [162], [132], [133] introduce an iterative threshold algorithm to locate the pupils by looking for two dark regions that satisfy certain anthropometric constraints using a skin-color model.”