Article
Version 1
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Eye Tracking Based on Event Camera and Spiking Neural Network
Version 1
: Received: 24 May 2024 / Approved: 24 May 2024 / Online: 24 May 2024 (10:11:28 CEST)
How to cite: Jiang, Y.; Wang, W.; Yu, L.; He, C. Eye Tracking Based on Event Camera and Spiking Neural Network. Preprints 2024, 2024051627. https://doi.org/10.20944/preprints202405.1627.v1 Jiang, Y.; Wang, W.; Yu, L.; He, C. Eye Tracking Based on Event Camera and Spiking Neural Network. Preprints 2024, 2024051627. https://doi.org/10.20944/preprints202405.1627.v1
Abstract
The event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of spiking neural network (SNN) with event camera, a near-range eye tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5\% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081ms, enhancing tracking stability while maintaining accuracy.
Keywords
event camera; eye tracking; spiking neuron network
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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