GPU-Accelerated High-Speed Eye Pupil Tracking System

Mompeán, Juan; Aragón, Juan L.; Prieto, Pedro M.; Artal, Pablo

doi:10.1109/sbac-pad.2015.17

Cited by 6 publications

(14 citation statements)

References 16 publications

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“…Despite being computationally costly, the pupil tracking based on Starburst algorithm is highly parallelizable and able to achieve up to 530 frames∕s with high-resolution images using a general purpose graphics processing unit. 11 Yuille et al 12 proposed a more complex model based on deformable templates, which represents the eyelids with two parabolas and the iris with a circle. This method was extended by Lam and Yan.…”

Section: Introductionmentioning

confidence: 99%

Robust eye tracking based on multiple corneal reflections for clinical applications

2018

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A set of methods in terms of both image processing and gaze estimation for accurate eye tracking is proposed. The eye-tracker used in this study relies on the dark-pupil method with up to 12 corneal reflections and offers an unprecedented high resolution imaging of the pupil and the cornea. The potential benefits of a higher number of glints and their optimum arrangement are analyzed considering distinct light sources configurations with 12, 8, 6, 4, and 2 corneal reflections. Moreover, a normalization factor of the pupil-glint vector is proposed for each configuration. There is a tendency for increasing accuracy with the number of glints, especially vertically (0.47 deg for 12 glints configuration versus 0.65 deg for 2 glints configuration). Besides the number of corneal reflections, their arrangement seems to have a stronger effect. A configuration that minimizes the interference of the eyelids with the corneal reflections is desired. Finally, the normalization of the pupil-glint vectors improves the vertical eye tracking accuracy up to 43.2%. In addition, the normalization also limits the need for a higher number of light sources to achieve better spatial accuracy.

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Section: Introductionmentioning

confidence: 99%

Robust eye tracking based on multiple corneal reflections for clinical applications

2018

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“…In this paper, the eye gaze tracking solution could be implemented in a pure hardware way. Compared with the PC-based and DSP-based solutions [2][3] , the proposed solution could reduce the CPU / GPU load and reduce system power consumption and cost. Compared with the FPGAbased solutions [4][5] , the proposed solution had a faster eye gaze tracking speed (the eye gaze tracking speed of paper [4] is 62.5Hz and the speed of paper [5] is 11Hz).…”

Section: Impactmentioning

confidence: 99%

30‐2: Implementation of a Real‐time Eye Gaze Tracking Solution for ASIC Based on VR Display

Yang

Sun

Gao

et al. 2018

Symp Digest of Tech Papers

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“…Since the memory bandwidth for the GPU is a scarce resource, we should try to reduce its usage as much as possible. Simply by changing the data type of the image pixels from integer (as we were using in [25]) to byte, the bandwidth is reduced to one-fourth for many memory operations. The resulting frames per second raises to 667.9 and the speedup increases to 30.4 × (31.7 × for the binocular setup).…”

Section: Reducing Memory Bandwidth Usagementioning

confidence: 99%

“…A preliminary version of a monocular pupil tracking approach was published in [25]. Compared with that conference version, this paper includes several extensions and improvements.…”

Section: Introductionmentioning

confidence: 99%

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Design of an accurate and high-speed binocular pupil tracking system based on GPGPUs

et al. 2017

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An efficient and robust pupil tracking system is an important tool in visual optics and ophthalmology. It is also central to techniques for gaze tracking, of use in psychological and medical research, marketing, human-computer interaction, virtual reality and other areas. A typical setup for pupil tracking includes a camera linked to infrared LED illumination. In this work, we evaluate and parallelize several pupil tracking algorithms with the aim of accurately estimating the pupil position and size in both eyes simultaneously, to be applied in a high-speed binocular pupil tracking system. To achieve high processing speed, the original non-parallel algorithms have been parallelized by using CUDA and OpenMP. Modern graphics processors are designed to process images at high temporal frequencies and spatial resolution, and CUDA enables them to be used for general-purpose computing. Our implementation allows for efficient binocular pupil tracking at high speeds for high-resolution images (up to 988 fps with images of 1280 × 1024 pixels) using a state-of-the-art GPU. Keywords High-speed eye pupil tracking • GPU • GPGPU • Image processing • Real-time systems • Gaze tracking

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GPU-Accelerated High-Speed Eye Pupil Tracking System

Cited by 6 publications

References 16 publications

Robust eye tracking based on multiple corneal reflections for clinical applications

Robust eye tracking based on multiple corneal reflections for clinical applications

30‐2: Implementation of a Real‐time Eye Gaze Tracking Solution for ASIC Based on VR Display

Design of an accurate and high-speed binocular pupil tracking system based on GPGPUs

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