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Temporal and spatial feature based approaches in drowsiness detection using deep learning technique
Journal of Real-Time Image Processing ( IF 3 ) Pub Date : 2021-04-30 , DOI: 10.1007/s11554-021-01114-x
Nageshwar Nath Pandey , Naresh Babu Muppalaneni

Drowsiness is a term which seems to be very simple but for a moment, it becomes a critical issue for many drivers and workers while they are performing their duty. Many people’s lives may collapse into trouble because of drowsiness. Therefore, such a real-time system is needed which can be easy to develop and configure for early as well as accurate drowsiness detection. As per requisite, we have adopted a large realistic dataset which includes 30 h video of 60 different participants in three classes, i.e. alert, low vigilant and drowsy. In our proposed work, we have selected the videos with extreme classes, i.e. alert and drowsy only. Further, we have designed two different models based on temporal and spatial feature by employing computer vision as well as deep-learning approach. In one model, temporal features are obtained by computer vision techniques followed by long short-term memory (LSTM) and the second model adopts spatial features extraction through convolution neural network (CNN) followed by LSTM. Although the temporal model is more complex and has less accuracy than spatial model, in spite of this, the study shows that the temporal model is far better in terms of training time than spatial model by establishing the comparison using confusion metrics and Area under Curve (AUC)–Receiver-Operating Characteristic Curve (ROC) score.



中文翻译:

使用深度学习技术的睡意检测中基于时空特征的方法

困倦这个词似乎很简单,但是暂时,对于许多驾驶员和工人在履行职责时,它已成为一个关键问题。许多人的生活可能由于困倦而陷入困境。因此,需要这样的实时系统,其可以容易地开发和配置以用于早期以及准确的睡意检测。根据需要,我们采用了一个大型的真实数据集,其中包括60个不同参与者的30小时视频,分为三个类别,即警觉,警惕和昏昏欲睡。在我们提出的工作中,我们选择了具有极端等级的视频,即仅警报和昏昏欲睡的视频。此外,我们通过采用计算机视觉和深度学习方法,设计了基于时空特征的两种不同模型。在一个模型中 时态特征是通过计算机视觉技术获得的,然后是长期短期记忆(LSTM),第二个模型采用了通过卷积神经网络(CNN)进行空间特征提取,然后是LSTM。尽管时间模型比空间模型更为复杂且准确性较差,但通过使用混淆度量和“曲线下面积”( AUC)–接收器操作特性曲线(ROC)得分。

更新日期:2021-04-30
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