Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
Source abstract
Abstract
On-road driving studies are essential for comprehending real-world driver behavior. This study investigates the use of eye-tracking (ET) technology in research on driver behavior and attention during Controlled Driving Studies (CDS). One significant challenge in these studies is accurately detecting when drivers divert their attention from crucial driving tasks. To tackle this issue, we present an improved method for analyzing raw gaze data, using a new algorithm for identifying ID tags called Binarized Area of Interest Tracking (BAIT). This technique improves the detection of incidents where the driver’s eyes are off the road through binarizing frames under different conditions and iteratively recognizing markers. It represents a significant improvement over traditional methods. The study shows that BAIT performs better than other software in identifying a driver’s focus on the windscreen and dashboard with higher accuracy. This study highlights the potential of our method to enhance the analysis of driver attention in real-world conditions, paving the way for future developments for application in naturalistic driving studies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gaze Tracking and Assistive Technology · Human-Automation Interaction and Safety · Visual Attention and Saliency Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Central Campus Győr, Széchenyi István University, H-9026 Gyor, Hungary
Eötvös Loránd Research Network, Piarista u. 4, H-1052 Budapest, Hungary
References (36)
Show source-provided references
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