MACHINE LEARNING BASED FEATURE EXTRACTION METHODS FOR ESTIMATION OF DRIVER'S DROWSINESS
Keywords:
Accident Sensor, Alcohol Sensor, Cell phone Detector, Eye Blinking, Head Movements, Machine LearningAbstract
At present, the number of vehicles in the cities are increasing rapidly and also which may cause accidents, so the prevention of accidents is a major challenge. According to autonomous vehicle technology and development, machine learning methods have been used to detect the driver‟s condition to improve the safety of the passengers and commuters in the road. Apart from the basic characteristics such as age, gender, driving experience, driver‟s previous accident history of the driver, driver's condition can be identified by considering the factors such as driver's facial expressions, eye blinking, head movements, usage of cell phone while driving, alcohol and accident sensor. Recent technologies such as video processing, image processing, and analysis using machine learning algorithms could be used to capture the constant images and videos of the driver to detect the behavior and to calculate the level of drowsiness. For example, a driver driving a long distance may feel tired and can be warned to take rest by giving a warning signal by an audio alarm. Thus, this work uses machine learning-based feature extraction methods for determining the drowsiness level of a driver.
Downloads
References
W. Deng and R. Wu, "Real-Time Driver-Drowsiness Detection System Using Facial Features," in
IEEE, vol. 7, (2019), pp. 118727-118738.
N. Panigrahi, K. Lavu, S. K. Gorijala, P. Corcoran and S. P. Mohanty, "A Method for Localizing the Eye Pupil for Point-of-Gaze Estimation,", IEEE Potentials, vol. 38, no. 1, (2019), pp. 37-42.
M. Kahlon and S. Ganesan, "Driver Drowsiness Detection System Based on Binary Eyes Image Data," IEEE International Conference on Electro/Information Technology (EIT), Rochester, MI, (2018), pp. 0209-0215.
H. U. Rehman, M. Naeem, M. Khan, G. Sikander and S. Anwar, "Eye Tracking based Real- Time Non-Interfering Driver Fatigue Detection System," 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, (2018), pp. 1-5.
S. Nanda, H. Joshi and S. Khairnar, "An IOT Based Smart System for Accident Prevention and Detection", Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, (2018), pp. 1-6.
F. Dachuan and T. Xinxing, "Driver Fatigue Detection Control System", 37th Chinese Control Conference (CCC), Wuhan, (2018), pp. 4378-4383.
O. Berkati and M. N. Srifi, "Predict Driver Fatigue Using Facial Features", International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Rabat, Morocco, (2018), pp. 1-5.
R. Huang, Y. Wang and L. Guo, "P-FDCN Based Eye State Analysis for Fatigue Detection”, IEEE 18thInternational Conference on Communication Technology (ICCT), Chongqing, (2018), pp. 1174-1178.
Omar Rigane, Karim Abbes, ChokriAbdelmoula, Mohamed Masmoudi, “A Fuzzy Based Method for Driver Drowsiness Detection”, IEEE/ACS 14th International Conference on Computer System and Applications (AICCSA), Hammamet, Tunisia,(2017),pp. 2161-5330.
AldilaRiztiane, David HabsaraHareva, Dina Stefani, Samuel Lukas, “Driver Drowsiness Detection Using Visual Information On Android Device”, International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), Denpasar, Indonesia, (2017), pp. 1752- 1913.
Omar Wathiq, Bhavna D. Ambudkar, “Optimized Driver Safety through driver Fatigue Detection methods”, International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, India, (2017), pp. 1759-1800.
B.M. Kusuma Kumari, P. Ramakanth Kumar, “A survey on drowsy driver detection system”, International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala, India, (2017), pp. 1-3.
Samra Naz, Aneeqa Ahmed, Qurat ul ain Mubarak, IrumNoshin, “Intelligent Driver Safety System Using Fatigue Detection”, 19th International Conference on Advanced Communication Technology (ICACT), Bongpyeong, South Korea, (2017), pp. 5-11.
B. Warwick, N. Symons, X. Chen and K. Xiong, "Detecting Driver Drowsiness using wireless Wearables", 12th International Conference on Mobile Ad Hoc Sensor Systems (MASS), (2015), pp. 585 - 588.
G. Li, B.-L. Lee, W.-Y. Chung, "Smartwatch-based wearable EEG system for driver drowsiness Detection", IEEE Sensors Journal, vol. 15, issue. 12, (2015), pp. 7169-7180.
S.-J. Jung, H.-S. Shin and W.-Y. Chung, "Driver fatigue and drowsiness monitoring system with Embedded electrocardiogram sensor on steering wheel", IET Intelligent Transportation Systems, vol. 8, issue. 1, (2014), pp. 43-50.
M. Omidyeganeh, A. Javadtalab and S. Shirmohammadi, "Intelligent Driver Drowsiness Detection through Fusion of Yawning and Eye Closure", Proc. IEEE International Conferences on Virtual Environment Human Computer Interfaces Measurement Systems, (2011), pp. 1-6.
A. Dasgupta, D. Rahman and A. Routray, "A Smartphone-based Drowsiness Detection and Warning system for Automotive Drivers", IEEE Transaction Intelligent Transportation Systems, vol. 20, issue 11, (2018), pp.4045 – 4054.
Zhang and C. Hua, "Driver fatigue recognition based on facial expression analysis using local binary patterns", Optik, vol. 126, no. 23, (2015), pp. 4501-4505.
A. Picot, S. Charbonnier, A. Caplier and N.-S. Vu, "Using retina modelling to characterize blinking: Comparison between EOG and video analysis", Machine Vision Appications, vol. 23, no. 6, (2015), pp. 1195-1208.
B. Akrout and W. Mahdi, "Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration", Machine Vision Applications, vol. 26, no. 1, (2015), pp. 1-13.
R. O. Mbouna, S. G. Kong and M.-G. Chun, "Visual analysis of eye state and head pose for driver alertness monitoring", IEEE Transactions on Intelligent Transportation System, vol. 14, no. 3, (2013), pp. 1462-1469.
Downloads
Published
How to Cite
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
