Hybrid FPGA-CPU pupil tracker.

PubMed ID: 34745752

Author(s): Kowalski B, Huang X, Steven S, Dubra A. Hybrid FPGA-CPU pupil tracker. Biomed Opt Express. 2021 Sep 22;12(10):6496-6513. doi: 10.1364/BOE.433766. eCollection 2021 Oct 1. PMID 34745752

Journal: Biomedical Optics Express, Volume 12, Issue 10, Oct 2021

An off-axis monocular pupil tracker designed for eventual integration in ophthalmoscopes for eye movement stabilization is described and demonstrated. The instrument consists of light-emitting diodes, a camera, a field-programmable gate array (FPGA) and a central processing unit (CPU). The raw camera image undergoes background subtraction, field-flattening, 1-dimensional low-pass filtering, thresholding and robust pupil edge detection on an FPGA pixel stream, followed by least-squares fitting of the pupil edge pixel coordinates to an ellipse in the CPU. Experimental data suggest that the proposed algorithms require raw images with a minimum of ∼32 gray levels to achieve sub-pixel pupil center accuracy. Tests with two different cameras operating at 575, 1250 and 5400 frames per second trained on a model pupil achieved 0.5-1.5 μm pupil center estimation precision with 0.6-2.1 ms combined image download, FPGA and CPU processing latency. Pupil tracking data from a fixating human subject show that the tracker operation only requires the adjustment of a single parameter, namely an image intensity threshold. The latency of the proposed pupil tracker is limited by camera download time (latency) and sensitivity (precision).