ArticleSensors (Basel, Switzerland)2019
Continuous Driver's Gaze Zone Estimation Using RGB-D Camera.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Gaze Zone Classification for Driving Studies Using YOLOv8 Image Classification.Sensors (Basel, Switzerland) · 2024Article
- Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.Sensors (Basel, Switzerland) · 2024Review
- Single Camera Face Position-Invariant Driver's Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Driver's Head Pose and Gaze Zone Estimation Based on Multi-Zone Templates Registration and Multi-Frame Point Cloud Fusion.Sensors (Basel, Switzerland) · 2022Article
- Stable Gaze Tracking with Filtering Based on Internet of Things.Sensors (Basel, Switzerland) · 2022Article
- Dual-Cameras-Based Driver's Eye Gaze Tracking System with Non-Linear Gaze Point Refinement.Sensors (Basel, Switzerland) · 2022Article
- Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The driver gaze zone is an indicator of a driver's attention and plays an important role in the driver's activity monitoring. Due to the bad initialization of point-cloud transformation, gaze zone systems using RGB-D cameras and ICP (Iterative Closet Points) algorithm do not work well under long-time head motion. In this work, a solution for a continuous driver gaze zone estimation system in real-world driving situations is proposed, combining multi-zone ICP-based head pose tracking and appearance-based gaze estimation. To initiate and update the coarse transformation of ICP, a particle filter with auxiliary sampling is employed for head state tracking, which accelerates the iterative convergence of ICP. Multiple templates for different gaze zone are applied to balance the templates revision of ICP under large head movement. For the RGB information, an appearance-based gaze estimation method with two-stage neighbor selection is utilized, which treats the gaze prediction as the combination of neighbor query (in head pose and eye image feature space) and linear regression (between eye image feature space and gaze angle space). The experimental results show that the proposed method outperforms the baseline methods on gaze estimation, and can provide a stable head pose tracking for driver behavior analysis in real-world driving scenarios.
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Registered trials
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