2016 · 13 citations · 11 references
ComputingAutomotive EngineeringComputer ScienceMachine LearningSensor FusionSensorsAutomotive SensorsFatigueFeature-level FusionPattern RecognitionSmart SensorsSupport Vector MachinesCamera-based SensingClassifier FusionCross-validationDriver Drowsiness DetectionDriver Monitoring SystemsFatigue DetectionMulti-sensor FusionMulti-sensor Systems
Fatigue during long-time driving threatens the safety of drivers and transportation. In this paper, we provide an effective method based on multi-sensor signals collected from Kinect2.0 camera and PPG pulse sensor to build a driver fatigue detection system. Unlike most traditional works, we define the transitional process of fatigue and elaborate its effect on training classifiers. The simulation experiments are then designed and 15 groups of data are collected. Our method works in the following steps: 1) feature extraction and fusion, 2) sample labelling and 3) SVM classifier designing. The 10-fold cross-validation accuracy of the classifier is 90.10% and the test accuracy is 83.82%. Experimental results verify that our method to deal with samples in transitional process is universal and more accurate than traditional methods. Moreover, our method based on multi-sensor works better than those dealing with single-sensor.
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Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology · 2011 · 41.3K citations