# An Embedded and Real-Time Pupil Detection Pipeline

## TL;DR

This paper presents an open-source, real-time embedded pupil detection system integrated into a wearable eye tracker, demonstrating hardware-software co-design for efficient, on-device eye analysis.

## Contribution

It introduces a novel embedded pupil detection pipeline and hardware design, enabling real-time analysis directly on wearable devices, which is a significant advancement over traditional off-device systems.

## Key findings

- Operates at 30fps with 54ms runtime at 480x640 resolution.
- Achieves an average detection error of 5.3368px on the LPW dataset.
- Provides a new dataset of 35,000 images for hardware-specific evaluation.

## Abstract

Wearable pupil detection systems often separate the analysis of the captured wearer's eye images for wirelessly-tethered back-end systems. We argue in this paper that investigating hardware-software co-designs would bring along opportunities to make such systems smaller and more efficient. We introduce an open-source embedded system for wearable, non-invasive pupil detection in real-time, on the wearable, embedded platform itself. Our system consists of a head-mounted eye tracker prototype, which combines two miniature camera systems with Raspberry Pi-based embedded system. Apart from the hardware design, we also contribute a pupil detection pipeline that operates using edge analysis, natively on the embedded system at 30fps and run-time of 54ms at 480x640 and 23ms at 240x320. Average cumulative error of 5.3368px is found on the LPW dataset for a detection rate of 51.9\% with our detection pipeline. For evaluation on our hardware-specific camera frames, we also contribute a dataset of 35000 images, from 20 participants.