Smartphone-based Eye Tracking System using Edge Intelligence and Model Optimisation

TL;DR

This paper introduces novel smartphone eye-tracking methods for video stimuli using CNN combined with RNNs, optimized with edge intelligence techniques to improve accuracy and real-time performance on resource-limited devices.

Contribution

It presents new CNN+RNN models for video-based eye tracking and applies model optimization techniques to enable real-time processing on smartphones.

Findings

CNN+LSTM achieved 0.955 cm error

Model inference time reduced by ~20% with quantisation

Optimizations improved energy and memory efficiency

Abstract

A significant limitation of current smartphone-based eye-tracking algorithms is their low accuracy when applied to video-type visual stimuli, as they are typically trained on static images. Also, the increasing demand for real-time interactive applications like games, VR, and AR on smartphones requires overcoming the limitations posed by resource constraints such as limited computational power, battery life, and network bandwidth. Therefore, we developed two new smartphone eye-tracking techniques for video-type visuals by combining Convolutional Neural Networks (CNN) with two different Recurrent Neural Networks (RNN), namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Our CNN+LSTM and CNN+GRU models achieved an average Root Mean Square Error of 0.955 cm and 1.091 cm, respectively. To address the computational constraints of smartphones, we developed an edge intelligence architecture to enhance the performance of smartphone-based eye tracking. We applied various optimisation methods like quantisation and pruning to deep learning models for better energy, CPU, and memory usage on edge devices, focusing on real-time processing. Using model quantisation, the model inference time in the CNN+LSTM and CNN+GRU models was reduced by 21.72% and 19.50%, respectively, on edge devices.