DriveCare: A Real-Time Vision Based Driver Drowsiness Detection Using Multiple Convolutional Neural Networks With Kernelized Correlation Filters (MCNN-KCF)
| Autor: | U Gopikrishnan, Renu Jose |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Facial expression Computer science business.industry media_common.quotation_subject 05 social sciences ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Convolutional neural network Video image Correlation Road transport Alertness 0502 economics and business 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence Real time vision business Vigilance (psychology) media_common |
| Zdroj: | 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA). |
| Popis: | Driver Drowsiness is one of the most significant safety issues facing the road transport industry today, and it reduces vigilance, alertness, and concentration to perform the attention-based activities such as driving. When the driver is in a fatigue state, the facial expressions are a bit different from those in the normal state. Here a new system called DriveCare, which determines the drivers’ fatigue state, like duration of eyes closure, blinking, and yawning, using video images, without equipping their bodies with any devices, is introduced. A novel face-tracking algorithm called Multiple Convolutional Neural Networks with Kernelized Correlation Filters (MCNN-KCF) is used to improve the tracking accuracy. Further, a detection method is used for extracting the facial features of the driver. Then with the help of these facial features, we evaluate the Driver’s state. By combining the features extracted by the eyes and mouth, DriveCare can alert the driver using a drowsiness warning tone. Different experimental results showed that the DriveCare achieved around a 95% accuracy. |
| Databáze: | OpenAIRE |
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