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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Driver Drowsiness Detection in Facial Images
摘要： Extracting effective features of fatigue in images and videos is an open problem. This paper introduces a face image descriptor that can be used for discriminating driver fatigue in static frames. In this method, first, each facial image in the sequence is represented by a pyramid whose levels are divided into non-overlapping blocks of the same size, and hybrid image descriptor are employed to extract features in all blocks. Then the obtained descriptor is filtered out using feature selection. Finally, non-linear SVM is applied to predict the drowsiness state of the subject in the image. The proposed method was tested on the public dataset NTH Drowsy Driver Detection (NTHUDDD). This dataset includes a wide range of human subjects of different genders, poses, and illuminations in real-life fatigue conditions. Experimental results show the effectiveness of the proposed method. These results show that the proposed hand-crafted feature compare favorably with several approaches based on the use of deep Convolutional Neural Nets.
关键词： hand-crafted features,supervised classification,Drowsiness detection,deep features
更新于2025-09-23 15:22:29
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Tests of a New Drowsiness Characterization and Monitoring System Based on Ocular Parameters
摘要： Drowsiness is the intermediate state between wakefulness and sleep. It is characterized by impairments of performance, which can be very dangerous in many activities and can lead to catastrophic accidents in transportation or in industry. There is thus an obvious need for systems that are able to continuously, objectively, and automatically estimate the level of drowsiness of a person busy at a task. We have developed such a system, which is based on the physiological state of a person, and, more specifically, on the values of ocular parameters extracted from images of the eye (photooculography), and which produces a numerical level of drowsiness. In order to test our system, we compared the level of drowsiness determined by our system to two references: (1) the level of drowsiness obtained by analyzing polysomnographic signals; and (2) the performance of individuals in the accomplishment of a task. We carried out an experiment in which 24 participants were asked to perform several Psychomotor Vigilance Tests in different sleep conditions. The results show that the output of our system is well correlated with both references. We determined also the best drowsiness level threshold in order to warn individuals before they reach dangerous situations. Our system thus has significant potential for reliably quantifying the level of drowsiness of individuals accomplishing a task and, ultimately, for preventing drowsiness-related accidents.
关键词： drowsy driving,drowsiness,photooculography,psychomotor vigilance test,monitoring,polysomnography
更新于2025-09-23 15:22:29
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Neuroergonomics || Drowsiness Detection During a Driving Task Using fNIRS
摘要： In this chapter we investigate the potential aspects of passive BCI for drowsiness detection. We analyze the drowsy state through the brain’s hemodynamic response measured using fNIRS (Hong et al., 2014). Signals from the dorsolateral prefrontal cortex were used for this purpose. To acquire the maximum classification accuracy, statistical features (signal peak and signal mean), calculated over 0–7 s time windows, were used for a passive BCI.
关键词： passive BCI,drowsiness detection,hemodynamic response,dorsolateral prefrontal cortex,fNIRS
更新于2025-09-10 09:29:36