Machine Learning Inspired Vision-based Drowsiness Detection using Eye and Body Motion Features

Ali Sheikh, J. Mir
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引用次数: 9

Abstract

Drowsiness-a state before the onset of sleep- resulting from insufficient s leep i s recognized a s a g lobal problem due to associated health and safety risks for the individuals involved in activities requiring constant attention. Therefore, several computer vision-based non-invasive techniques have been proposed for the timely detection of drowsiness. However, these methods are generally based on drowsy behavior indicators like yawning and excessive eye blinking. Moreover, the results are generally reported for databases with very few subjects or acted drowsy data. This paper proposes a drowsiness detection technique based on hybrid features using comprehensive and challenging real drowsy data. Primarily, eye state and body motion analysis is performed to determine drowsiness. Towards ameliorating this, the eye region is selected from each frame using facial landmarks and is described using a histogram of oriented gradients (HoG) descriptors. For body motion description, frame difference is computed and parameterized using HoG descriptors. Then, the hybrid feature set, i.e., the combination of eye and body motion features, is subjected to dimensionality reduction through principal component analysis. Finally, SVM is trained and tested on the hybrid feature set to detect drowsiness. The detection accuracy of 90% is achieved through our proposed technique.
机器学习启发的基于视觉的困倦检测,使用眼睛和身体运动特征
由于睡眠不足而导致的困倦是一种未进入睡眠的状态,它被认为是一个全球性问题,因为参与需要持续关注的活动的个人会面临相关的健康和安全风险。因此,人们提出了几种基于计算机视觉的非侵入性技术来及时检测睡意。然而,这些方法通常是基于昏昏欲睡的行为指标,如打哈欠和过度眨眼。此外,通常报告的结果是针对具有很少主题或行为困倦数据的数据库。本文提出了一种基于混合特征的困倦检测技术,该技术利用了全面且具有挑战性的真实困倦数据。首先,通过眼睛状态和身体运动分析来确定睡意。为了改善这一点,使用面部地标从每帧中选择眼睛区域,并使用定向梯度直方图(HoG)描述符进行描述。对于身体运动描述,使用HoG描述符计算帧差并进行参数化。然后,通过主成分分析对混合特征集进行降维,即眼睛和身体运动特征的组合。最后,在混合特征集上训练和测试支持向量机以检测困倦。通过我们提出的技术,检测精度达到90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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