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首页> 外文期刊>IEEE sensors journal >Hybrid Embedded-Systems-Based Approach to in-Driver Drunk Status Detection Using Image Processing and Sensor Networks
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Hybrid Embedded-Systems-Based Approach to in-Driver Drunk Status Detection Using Image Processing and Sensor Networks

机译：基于混合的嵌入式系统的驾驶员醉酒状态检测方法使用图像处理和传感器网络

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摘要

Car drivers under the influence of alcohol is one of the most common causes of road traffic accidents. To tackle this issue, an emerging, suitable alternative is the use of intelligent systems-traditionally based on either sensor networks or artificial vision-that are aimed to prevent starting the car when drunk status on the car driver is detected. In such vein, this paper introduces a system whose main objective is identifying a person having alcohol in the blood through supervised classification of sensor-generated and computer-vision-based data. To do so, some drunk-status criteria are considered, namely: the concentration of alcohol in the car environment, the facial temperature of the driver and the pupil width. Specifically, for data acquisition purposes, the proposed system incorporates a gas sensor, temperature sensor and a digital camera. Acquired data are analyzed into a two-stages machine learning system consisting of feature selection and supervised classification algorithms. Both acquisition and analysis stages are to be performed into a embedded system, and therefore all procedures and algorithms are designed to work at low-computational resources. As a remarkable outcome, due mainly to the incorporation of feature selection and relevance analysis stages, proposed approach reaches a classification performance of 98% while ensures adequate operation conditions for the embedded system.
机译：酒精影响下的汽车司机是道路交通事故最常见的原因之一。为了解决这个问题，一种新兴的合适的替代方案是使用智能系统 - 传统上基于传感器网络或人工视觉 - 旨在防止在检测到汽车驾驶员上的醉酒状态时启动汽车。在这种静脉中，本文介绍了一种系统，其主要目标是通过监督传感器生成和基于计算机视觉的数据的分类来识别血液中具有酒精的人。为此，考虑了一些醉酒状态标准，即：汽车环境中的酒精浓度，驾驶员的面部温度和瞳孔宽度。具体地，对于数据采集目的，所提出的系统包括气体传感器，温度传感器和数码相机。获取的数据被分析到由特征选择和监督分类算法组成的双级机器学习系统中。既要进行采集和分析阶段都要进入嵌入式系统，因此所有程序和算法都设计用于低计算资源。作为一个显着的结果，主要是由于具有特征选择和相关性分析阶段的纳入，所提出的方法达到98％的分类性能，同时确保嵌入式系统的适当运行条件。

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