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GazeCapsNet:一个轻量级的凝视估计框架

Shakhnoza Muksimova¹, Yakhyokhuja Valikhujaev², Sabina Umirzakova¹

¹Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea.

Sensors (Basel, Switzerland)

|February 26, 2025

概括

移动-GazeCapsNet通过将囊网络与轻量级架构集成,为移动设备提供高效和准确的目光估计. 这种新的框架通过实时处理能力实现了最先进的性能.

科学领域:

计算机视觉计算机视觉
人与计算机的交互
机器学习机器学习

背景情况:

视线估计对于VR,AR和驾驶员监控至关重要,但由于计算需求,当前的模型在移动部署方面遇到了困难.
现有的方法往往需要复杂的预处理或大量的资源,限制了它们在移动设备上的使用.

研究的目的:

为移动应用程序引入Mobile-GazeCapsNet,一个高效和准确的目光估计框架.
通过利用囊网络和轻量级架构来克服现有模型的局限性.

主要方法:

通过将囊网络与MobileNet v2,MobileOne和ResNet-18.8集成,开发了移动-GazeCapsNet.
实现了自我注意路由 (SAR) 来取代代路由,动态分配计算资源以提高效率.
消除了对面部地标检测的需求.

主要成果:

在ETH-XGaze和Gaze360数据集上实现了最先进的 (SOTA) 性能,平均角误差 (MAE) 减少了高达15%.
实时处理在每20毫秒的时间.
仅需要1170万个参数,使其适用于资源有限的环境.

结论:

移动-GazeCapsNet提供了一个实用和有效的解决方案,用于实时移动凝视估计.
该框架为移动视线估计技术设定了一个新的标准,平衡准确性和效率.

关键词:

囊网络是一种囊网络.眼睛的外表眼睛的外表凝视的估计估计.轻量级架构中的轻量级架构.自我注意路由机制的路由机制.

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