Detection of yawning in driver behavior based a convolutional neural network
Annotation
Among the factors that usually cause road accidents in the world is driver fatigue, which accumulates during the trip or is present even before it begins. One of the most common signs of fatigue or tiredness of a vehicle driver is yawning. The detection of signs of yawning in human behavior is potentially able to further characterize its state of fatigue. Computer image processing methods are actively used to detect the openness of the mouth and yawning for a person. However, this approach has many disadvantages, which include different environmental conditions and a variety of situational yawning options for different people. The paper presents a scheme of a detector for determining signs of yawning, which is focused on processing images of the driver’s face using data analysis methods, computer image processing, and a convolutional neural network model. The essence of the proposed method is to detect yawning in the driver’s behavior in the cabin of a vehicle based on the analysis of a sequence of images obtained from a video camera. It is shown that the driver’s yawning state is accompanied by a wide and prolonged openness of the mouth. Prolonged openness of the mouth signals the appearance of signs of yawning. A conceptual model for detecting the openness of the mouth for a vehicle driver is presented and a scheme for processing and labeling the YawDD and Kaggle Drowsiness Dataset datasets is developed. The developed convolutional neural network model showed an accuracy of 0.992 and recall of 0.871 on a test 10 % data set. The proposed scheme for detecting the yawning state has been validated on a test video subset extracted from the YawDD: Yawning Detection Dataset. This detection scheme successfully detected 124 yawns among all video files from the test dataset. The proportion of correctly classified objects is 98.2 % accuracy, precision is equal to 96.1 %, recall is 98.4 %, and F score is 97.3 % while detecting signs of yawning in driver behavior. Detecting signs of yawning in the driver’s behavior allows one to clarify information about the driver and thereby to increase the effectiveness of existing driver monitoring systems in the vehicle cabin, aimed at preventing and reducing the risk of road accidents. The proposed approach can be combined with other technologies for monitoring driver behavior when building an intelligent driver support system.
Keywords
Постоянный URL
Articles in current issue
- A study of a silicone film deposited on quartz glass under laser radiation
- Optical composites based on organic polymers and semiconductor pigments
- A new algorithm for the identification of sinusoidal signal frequency with constant parameters
- A study of silicon p-n structures with mono and multifacial photosensitive surfaces
- A Game Theory approach for communication security and safety assurance in cyber-physical systems with Reputation and Trust-based mechanisms
- A study of the influence of human factors on the speed of urban rail transport
- An algorithm for detecting RFID-duplicates
- Reduction of LSB detectors set with definite reliability
- Classification of objects in images with distortions based on a two-stage topological analysis
- Dimensionality reduction of the attributes using fuzzy optimized independent component analysis for a Big Data Intrusion Detection System
- An optimal swift key generation and distribution for QKD
- A study of vectorization methods for unstructured text documents in natural language according to their influence on the quality of work of various classifiers
- Recognition the emotional state based on a convolutional neural network
- Intellectualization of personnel development management in high-tech service-oriented companies
- A study of the efficiency of the magnetic compass correction system
- A new analytical model of drain current and small signal parameters for AlGaN-GaN high-electron-mobility transistors
- Imputation and system modeling of acid-base state parameters for different groups of patients
- Construction of movement trajectories for objects based on the Dubins car problem, taking into account constant external influences
- A mathematical model of an epidemic with an arbitrary law of recovery
- Simulation of the pulsed outflow of air and fine powder mixture, partially filling the discharge channel
- Vectorized numerical algorithms for the solution of continuum mechanics problems
- A comparative analysis of computational intelligence algorithms for estimation of LTE channels
- Implementation of a clinical decision support system to improve the medical data quality for hypertensive patients