ArticleSensors (Basel, Switzerland)2021
Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental Scenarios.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Review
- Responses of Vehicular Occupants During Emergency Braking and Aggressive Lane-Change Maneuvers.Sensors (Basel, Switzerland) · 2024Article
- Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator.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
- Dual-Cameras-Based Driver's Eye Gaze Tracking System with Non-Linear Gaze Point Refinement.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
Funding
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Abstract
Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver's gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases.
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