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Smartphone-based fatigue detection system using progressive locating method

机译:基于渐进定位方法的基于智能手机的疲劳检测系统

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摘要

Smartphone applications became very popular nowadays as they provide useful functionalities to our daily lives over ordinary voice services. They offer a small but powerful computing platform where intelligent algorithms can be coded into some live-saving products which profoundly impact our daily lifestyles. A mobile fatigue detection system which is proposed in this study is an important life-saving application running on smartphone. Mobile detection system faces technological challenges such as: (i) embracing relatively low-resolution images in image recognition; (ii) supporting fast response time given a low-power CPU in comparison to a desktop computer; and (iii) demanding for high prediction accuracy by light-weight machine learning algorithms, as the software programs embedded in smartphones is resource constrained. Solutions with respect to indoor facial profiling system which are mainly based on progressive locating method for eye detection are discussed in this study. Acceptable experimental results in terms of eye detection rate and driver fatigue detection in different situations are presented.
机译:如今,智能手机应用程序变得越来越流行,因为它们比普通的语音服务为我们的日常生活提供了有用的功能。它们提供了一个小型但功能强大的计算平台,该平台可以将智能算法编码为一些可以拯救生命的产品,这些产品对我们的日常生活产生了深远的影响。本研究提出的移动疲劳检测系统是在智能手机上运行的重要救生应用。移动检测系统面临着技术挑战,例如:(i)在图像识别中采用分辨率较低的图像; (ii)与台式计算机相比,在低功耗CPU的情况下支持快速响应时间; (iii)轻量级机器学习算法要求高预测精度,因为嵌入在智能手机中的软件程序受资源限制。本研究讨论了主要基于渐进式定位方法进行室内人脸轮廓分析的解决方案。提出了在不同情况下在眼睛检测率和驾驶员疲劳检测方面可接受的实验结果。

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