FAPNet: An Effective Frequency Adaptive Point-based Eye Tracker

TL;DR

FAPNet introduces a frequency adaptive point-based eye tracker utilizing event cameras, achieving high accuracy and efficiency by leveraging sparse, high-temporal-resolution data and novel architectural mechanisms for resource-limited environments.

Contribution

The paper proposes FAPNet, a novel frequency adaptive point-based architecture for event-based eye tracking that improves accuracy and reduces computational load on edge devices.

Findings

Achieves 97.95% accuracy on the Event-based Eye Tracking Challenge.

Consumes only 10% of PEPNet's computational resources on the SEET dataset.

Independent of sensor resolution, suitable for resource-constrained devices.

Abstract

Eye tracking is crucial for human-computer interaction in different domains. Conventional cameras encounter challenges such as power consumption and image quality during different eye movements, prompting the need for advanced solutions with ultra-fast, low-power, and accurate eye trackers. Event cameras, fundamentally designed to capture information about moving objects, exhibit low power consumption and high temporal resolution. This positions them as an alternative to traditional cameras in the realm of eye tracking. Nevertheless, existing event-based eye tracking networks neglect the pivotal sparse and fine-grained temporal information in events, resulting in unsatisfactory performance. Moreover, the energy-efficient features are further compromised by the use of excessively complex models, hindering efficient deployment on edge devices. In this paper, we utilize Point Cloud as the event representation to harness the high temporal resolution and sparse characteristics of events in eye tracking tasks. We rethink the point-based architecture PEPNet with preprocessing the long-term relationships between samples, leading to the innovative design of FAPNet. A frequency adaptive mechanism is designed to realize adaptive tracking according to the speed of the pupil movement and the Inter Sample LSTM module is introduced to utilize the temporal correlation between samples. In the Event-based Eye Tracking Challenge, we utilize vanilla PEPNet, which is the former work to achieve the $p_{10}$ accuracy of 97.95\%. On the SEET synthetic dataset, FAPNet can achieve state-of-the-art while consuming merely 10\% of the PEPNet's computational resources. Notably, the computational demand of FAPNet is independent of the sensor's spatial resolution, enhancing its applicability on resource-limited edge devices.