Determining driver visual attention with one camera

By Paul Smith, Mubarak Shah, Niels da Vitoria Lobo. IEEE transactions on intelligent transportation systems (Print), 2003

348 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/148ee92fbecc32cddcca9091661b8a81e7d32390

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

In Human-Computer Interaction

Citations in the corpus

Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines (2007, background): “[11] developed a robust system using head orientation and eye blinks to monitor decrements in visual attention due to fatigue while driving.”
Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles (2007, methodology): “Smith et al. [ 19 ] developed a system for capturing driver visual attention using Finite State Automata.”
Human-Computer Interaction: Overview on State of the Art (2008, neutral): “• Intelligent Homes/Offices [60] • Driver Monitoring [ 61 ]”
Detecting Driver Drowsiness Based on Sensors: A Review (2012, background): “However, research on using other behavioral measures, such as yawning [51] and head or eye position orientation [52,53], to determine the level of drowsiness is ongoing (Table 2).”
Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness (2010, methodology): “In another system, the driver's eyes and lip corners were initialized with color predicates and tracked in relation to a bounding box around the driver's head [10].”
Real-time system for monitoring driver vigilance (2004, methodology): “Another successful head/eye monitoring and tracking of drivers system to detect drowsiness using of one camera, and based on color predicates, is presented in [13].”
Multimodal Human Computer Interaction: A Survey (2005, background): “For example, the authors of [168] monitor driver visual attention using a single, non-wearable camera placed on a car’s dashboard to track face features and for gaze detection.”
Online Driver Distraction Detection Using Long Short-Term Memory (2011, background): “By characterizing distraction on a per-drive basis, we smooth out these short intervals of attention in order to model the driver state on a long-term basis which in turn is desired when using driver state predictions for adaptive lane keeping assistance.”