6,758 research outputs found
A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding
Driver gaze plays an important role in different gaze-based applications such
as driver attentiveness detection, visual distraction detection, gaze behavior
understanding, and building driver assistance system. The main objective of
this study is to perform a comprehensive summary of driver gaze fundamentals,
methods to estimate driver gaze, and it's applications in real world driving
scenarios. We first discuss the fundamentals related to driver gaze, involving
head-mounted and remote setup based gaze estimation and the terminologies used
for each of these data collection methods. Next, we list out the existing
benchmark driver gaze datasets, highlighting the collection methodology and the
equipment used for such data collection. This is followed by a discussion of
the algorithms used for driver gaze estimation, which primarily involves
traditional machine learning and deep learning based techniques. The estimated
driver gaze is then used for understanding gaze behavior while maneuvering
through intersections, on-ramps, off-ramps, lane changing, and determining the
effect of roadside advertising structures. Finally, we have discussed the
limitations in the existing literature, challenges, and the future scope in
driver gaze estimation and gaze-based applications
Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction
This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl
A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms
In this paper a review is presented of the research on eye gaze estimation
techniques and applications, that has progressed in diverse ways over the past
two decades. Several generic eye gaze use-cases are identified: desktop, TV,
head-mounted, automotive and handheld devices. Analysis of the literature leads
to the identification of several platform specific factors that influence gaze
tracking accuracy. A key outcome from this review is the realization of a need
to develop standardized methodologies for performance evaluation of gaze
tracking systems and achieve consistency in their specification and comparative
evaluation. To address this need, the concept of a methodological framework for
practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July
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