Estimating Driver Attentiveness Through Head Pose Using Hybrid Geometric-Based Method

Vankayalapati, Hima Deepthi; Anne, Koteswara Rao; Harsha, S. Sri

doi:10.1007/978-981-16-9669-5_18

Cited by 1 publication

(1 citation statement)

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“…The visual axis is more sensitive to focal length, relative positions among the camera, display screen, and offset angles, and it is still a challenging task to obtain gaze direction estimation accurately by using custom-made devices with one camera and a small display screen. Most existing gaze estimation techniques include model-based or feature-based techniques [ 14 , 15 , 16 ]. The geometric models of the eyeball and its environment extract eye features (corneal infrared reflections, pupil center and iris contours) to fit the model.…”

Section: Related Workmentioning

confidence: 99%

A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications

Vankayalapati

Kuchibhotla

Chadalavada

et al. 2022

Sensors

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A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human–machine or human–robot interactions. For example, in high-accuracy advent applications such as Driver’s Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.

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Section: Related Workmentioning

confidence: 99%