Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks
نویسندگان
چکیده
Statistics have shown that 20% of all road accidents are fatigue-related, and drowsy detection is a car safety algorithm that can alert a snoozing driver in hopes of preventing an accident. This paper proposes a deep architecture referred to as deep drowsiness detection (DDD) network for learning effective features and detecting drowsiness given a RGB input video of a driver. The DDD network consists of three deep networks for attaining global robustness to background and environmental variations and learning local facial movements and head gestures important for reliable detection. The outputs of the three networks are integrated and fed to a softmax classifier for drowsiness detection. Experimental results show that DDD achieves 73.06% detection accuracy on NTHU-drowsy driver detection benchmark dataset.
منابع مشابه
Detecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
متن کاملThe Mechanical Design of Drowsiness Detection Using Color Based Features
This paper demonstrates design and fabrication o f a mechatronic system for human drowsiness detection. This system can be used in multiple places. For example, in factories, it is used on some dangerous machinery and in cars in order t o prevent the operator o r driver from falling asleep. This system is composed of three parts: (1) mechanical, (2) electrical and (3) image processing system. A...
متن کاملDriver Drowsiness Detection by Identification of Yawning and Eye Closure
Today most accidents are caused by drivers’ fatigue, drowsiness and losing attention on the road ahead. In this paper, a system is introduced, using RGB-D cameras to automatically identify drowsiness and give warning. In this system two important modules have been utilized simultaneously to identify the state of driver’s mouth and eyes for detecting drowsiness. At first, using the depth informa...
متن کاملCrop Land Change Monitoring Based on Deep Learning Algorithm Using Multi-temporal Hyperspectral Images
Change detection is done with the purpose of analyzing two or more images of a region that has been obtained at different times which is Generally one of the most important applications of satellite imagery is urban development, environmental inspection, agricultural monitoring, hazard assessment, and natural disaster. The purpose of using deep learning algorithms, in particular, convolutional ...
متن کاملImproving Accuracy in Intrusion Detection Systems Using Classifier Ensemble and Clustering
Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be cons...
متن کامل