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2020 | OriginalPaper | Buchkapitel

Efficiency in Real-Time Webcam Gaze Tracking

verfasst von : Amogh Gudi, Xin Li, Jan van Gemert

Erschienen in: Computer Vision – ECCV 2020 Workshops

Verlag: Springer International Publishing

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Abstract

Efficiency and ease of use are essential for practical applications of camera based eye/gaze-tracking. Gaze tracking involves estimating where a person is looking on a screen based on face images from a computer-facing camera. In this paper we investigate two complementary forms of efficiency in gaze tracking: 1. The computational efficiency of the system which is dominated by the inference speed of a CNN predicting gaze-vectors; 2. The usability efficiency which is determined by the tediousness of the mandatory calibration of the gaze-vector to a computer screen. To do so, we evaluate the computational speed/accuracy trade-off for the CNN and the calibration effort/accuracy trade-off for screen calibration. For the CNN, we evaluate the full face, two-eyes, and single eye input. For screen calibration, we measure the number of calibration points needed and evaluate three types of calibration: 1. pure geometry, 2. pure machine learning, and 3. hybrid geometric regression. Results suggest that a single eye input and geometric regression calibration achieve the best trade-off.

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Titel: Efficiency in Real-Time Webcam Gaze Tracking
verfasst von: Amogh Gudi
Xin Li
Jan van Gemert
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2020 Workshops
Print ISBN: 978-3-030-66414-5

Electronic ISBN: 978-3-030-66415-2

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-66415-2_34

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