IJIGSP Vol. 12, No. 3, 8 Jun. 2020
Cover page and Table of Contents: PDF (size: 582KB)
Full Text (PDF, 582KB), PP.41-50
Views: 0 Downloads: 0
Drowsiness, smartphone-based, desktop-based, driver drowsiness detection, face tracking and feature extraction
Driver drowsiness is the momentous factor in a huge number of vehicle accidents. This driver drowsiness detection system has been valued highly and applied in various fields recently such as driver visual attention monitoring and driver activity tracking. Drowsiness can be detected through the driver face monitoring system. Nowadays smartphone-based application has developed rapidly and thus also used for driver safety monitoring system. In this paper, a detailed review of driver drowsiness detection techniques implemented in the smartphone has been reviewed. The review has also been focused on insight into recent and state-of-the-art techniques. The advantages and limitations of each have been summarized. A comparative study of recently implemented smartphone-based approaches and mostly used desktop-based approaches have also been discussed in this review paper. And the most important thing is this paper helps others to decide better techniques for the effective drowsiness detection.
Anis-Ul-Islam Rafid, Amit Raha Niloy, Atiqul Islam Chowdhury, Nusrat Sharmin, " A Brief Review on Different Driver's Drowsiness Detection Techniques", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.12, No.3, pp. 41-50, 2020. DOI: 10.5815/ijigsp.2020.03.05
[1]Luis M. Bergasa, Daniel Almería, Javier Almazán, J. Javier Yebes, Roberto Arroyo, DriveSafe: An app for alerting inattentive drivers and scoring driving behaviors. 2014 IEEE Intelligent Vehicles Symposium Proceedings, IEEE, ISSN: 1931-0587, July 2014
[2]Lunbo Xu, Shunyang Li, Kaigui Bian, Tong Zhao, Wei Yan, Sober-Drive: A smartphone-assisted drowsy driving detection system. 2014 International Conference on Computing, Networking and Communications (ICNC), IEEE, April 2014
[3]Chin-Teng Lin, Chun-Hsiang Chuang, Chih-Sheng Huang, Shu-Fang Tsai, Shao-Wei Lu, Yen-Hsuan Chen, Wireless and Wearable EEG System for Evaluating Driver Vigilance. IEEE Transactions on Biomedical Circuits and Systems, Vol.8 , Issue: 2 , April 2014
[4]Gang Li, Wan-Young Chung, Estimation of Eye Closure Degree Using EEG Sensors and Its Application in Driver Drowsiness Detection. Vol.14, Issue: 9, Sensors, September 2014
[5]Zhongyang Chen, Jiadi Yu, Yanmin Zhu, Yingying Chen, Minglu Li, D3: Abnormal driving behaviors detection and identification using smartphone sensors. 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking, IEEE, November 2015
[6]German Castignani, Thierry Derrmann, Raphael Frank, Thomas Engel, Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring. IEEE Intelligent Transportation Systems Magazine, Vol. 7, Issue: 1, January 2015
[7]Tatsuaki Osafune, Toshimitsu Takahashi, Noboru Kiyama, Tsuneo Sobue, Hirozumi Yamaguchi, Teruo Higashino, Analysis of Accident Risks from Driving Behaviors. International Journal of Intelligent Transportation Systems Research, Vol.15, Issue 3, pp 192–202, September 2017
[8]Jair Ferreira Júnior, Eduardo Carvalho, Bruno V. Ferreira, Cleidson de Souza, Yoshihiko Suhara ,Alex Pentland, Gustavo Pessin, Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS ONE 12(4): e017495, April 2017
[9]Eddie E. Galarza, Franklin M. Silva, Paola M. Velasco, Eddie D. Galarza, Real Time Driver Drowsiness Detection Based on Driver’s Face Image Behavior Using a System of Human Computer Interaction Implemented in a Smartphone. Proceedings of the International Conference on Information Technology & Systems (ICITS 2018) pp 563-572, January 2018
[10]Faisal Mohammad, Kausalendra Mahadas, George K. Hung, Drowsy driver mobile application: Development of a novel scleral-area detection method. Computers in Biology and Medicine, Vol.89, October 2017, Pages 76-83
[11]Jiadi Yu, Zhongyang Chen, Yanmin Zhu, Yingying (Jennifer) Chen, Linghe Kong, Minglu Li, Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones. IEEE Transactions on Mobile Computing, Vol.16, Issue: 8, August 2017
[12]F. Moreno, F. Aparicio, W. Hemandez and J. Paez, “A Low-cost Real-Time FPGA Solution for Driver Drowsiness Detection”, Proceedings of 29th IEEE Annual Conference of the Industrial Electronics Society, (2003) November, Virginia, USA
[13]S. Thorat, P. Nagare, S. Mulay, Drowsiness Detection Raspberry PI 3 model B. International Jounal of Computer Engineering & Applications, May 2018
[14]Z. Zhang and J. Zhang, A New Real-Time Eye Tracking for Driver Fatigue Detection. 6th international conference on ITS telecommunication proceedings, IEEE
[15]Ying-Chu kuo, wen-Ling Hsu, Real-Time Drowsiness Detection System for Intelligent Vehicles.National Chin-Yi university Institutional repository.
[16]Brendon O’Brien, A Look at CCTV in Dublin. Intelligent Transport, October 2007
[17]Chandraprakash Sahoo, Driver drowsiness detection System. Ethesis @ nit Rourkela, 2016
[18]Paul Viola and Michael Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, Conference on computer vision and pattern recognition, 2001
[19]Karin sobottka and Ioannis Pitas, A Novel Method for Automatic Face Segmentation, Facial Feture Extraction and Tracking. Published in Signal Processing: Image Communication, Vol.12, No.3, pp.263-281,1998
[20]Harini Veeraraghavan and Nikolaos P. Papanikolopoulos, Detecting Driver Fatigue Through the Use of Advanced Face Monitoring Techniques. ITS Institute Center for Transportation Studies 200 Transportation and Safety Building, 2001
[21]Deepak Ghimire, Sunghwan Jeong, Sunhong Yoon, Sanghyun Park, Juhwan Choi, Real-Time Sleepiness Detection for Driver State Monitoring System. Advanced Science and Technology Letters Vol.120 (GST 2015), pp.1-8
[22]Marco J. Flores, Arturo de la Escalera, J.M. Armingol, Real-time Warning System for driver Drowsiness Detection Using Visual Information. Journal of Intelligent and Robotics Systems, 59(2):103-205, August 2010
[23]Aleksandar Čolić, Oge Marques, Borko Furht, Design and implementation of a driver drowsiness detection system : A practical approach, International Conference on Signal Processing and Multimedia Applications (SIGMAP),IEEE, August 2014
[24]C. Murukesh, Preethi Padmanabhan, Drowsiness Detection for Drivers Using Computer Vision. WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS, Vol. 12, 2015
[25]Kartik Dwivedi, Kumar Biswaranjan, amit Sethi, Drowsy driver detection using representation learning. 2014 IEEE International Advance Computing Conference (IACC), IEEE, February 2014
[26]Mohamad-Hoseyn sigari, Mahmood Fathy, Mohsen Soryani, A Driver Face Monitoring System for Fatihue and Distraction Detection. International journal of Vehicular Technology, 2013
[27]Xiao fan, Bao-Cai Yin, Yan-Feng Sun, Yawning Detection for Monitoring driver Fatigue. International Conference on Machine Learning and Cybernetics, IEEE, August 2007
[28]Lingling Li, Yangzhou Chen, zhenlong Li, Yawning detection for monitoring driver fatigue based on two cameras. 12th International IEEE Conference on Intelligent Transportation Systems, 2009
[29]Tayyaba Azim, M. Arfan Jaffar, Anwar Majid Mirza, Automatic Fatigue of Drivers through Pupil detection and Yawning. Fourth International Conference on Innovative Computing, Information and Control (ICICIC), IEEE, 2009
[30]Phillip Ian Wilsom, Dr John Fernandez, Facial feature detection using Haar classifiers. Journal of Computing Sciences in Colleges, vol. 21, researchgate, 2006
[31]Shan An, Xin Ma, Rui Song, Yibin li, Face detection and recognition with surf for human-robot Interaction. IEEE International Conference on Automation and Logistics, August 2009
[32]Nitin Sharma, Text Extraction and Recognition from the Normal Images using MSER Feature Extraction and Text Segmentation Methods. Indian Journal of science & Technology, Vol. 10, May 2017
[33]Muhammad Sharif, Muhammad Younus Javed, Sajjad Mohsin, Face Recognition Based on Facial Features. Research Journal of Applied Sciences,Engineering and Technology, August 2012
[34]Farman Ali, Sajid Ullah Khan, Muhammad Zarrar Mahmudi, Rahmat Ullah, A Comparison of Fast, Surf, Eigen, Harris, and Mser features. International Journal of Computer Engineering and Information Technology (IJCEIT), Vol. 8, June 2016
[35]Qi Cao, ruishan Liu, Real-Time Face Tracking and Replacement. Stanford University. CA 94305 qcao@stanford.edu Ruishan Liu Department of EE.
[36]Ebrahim Emami, Mahmood Fathy, Ehsan Kozegar, Online failure detection and correction for CAMShift tracking algorithm. 8th Iranian Conference on Machine Vision and Image Processing (MVIP),IEEE, September 2013
[37]F.Abdat, C.Maaoui, A.Pruski, Real time facial feature points tracking with Pyramidal Lucas-Kanade algorithm. RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication, IEEE, August 2008
[38]Ritesh Boda and M. Jasmine Pemeena Priyadarsini, FACE DETECTION AND TRACKING USING KLT AND VIOLA JONES. ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 23, December 2016
[39]P.K. Turaga, G. Singh, P.K. Bora, Face tracking using Kalman filter with dynamic noise statistics. IEEE Region 10 Conference TENCON 2004, November 2004.
[40]Saranya M, Padmavathi S, Face Tracking in Video by Using Kalman Filter. International Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol. 4, Issue 6( Version 3), June 2014, pp.54-58
[41]Mohamad-Hoseyn Sigari, Mahmood Fathy, Mohsen Soryani, A Driver Face Monitoring System for Fatigue and Distraction Detection. Hindawi Publishing Corporation International Journal of Vehicular Technology Volume 2013, Article ID 263983, November 2012
[42]J. C. McCall and M. M. Trivedi, “Facial Action Coding Using Multiple Visual Cues and a Hierarchy of Particle Filters”, Proceeding of IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), (2006), New York, USA
[43]Q. Ji and X. Yang, “Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance”, RealTime Imaging, vol. 8, (2002)
[44]L. M. Bergasa, J. Nuevo, M. A. Sotelo and M. Vhzquez, “Weal-Time System for Monitoring Driver Vigilance”, Proceeding of IEEE Intelligent Vehicles Symposium, (2004) June, Parma, Italy
[45]L. M. Bergasa and J. Nuevo, “Real-Time System for Monitoring Driver Vigilance”, IEEE International Symposium on Industrial Electronics, (2005) June, Dubrovnik, Croatia
[46]L. M. Bergasa, J. Nuevo, M. A. Sotelo, R. Barea and M. E. Lopez, “Real-Time System for Monitoring Driver Vigilance”, IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, (2006).
[47]Z. Zhang and J. Zhang, “Driver Fatigue Detection Based Intelligent Vehicle Control”, Proceeding of 18th International Conference on Pattern Recognition (ICPR), (2006) September, Hong Kong, China
[48]T. Wang and P. Shi, “Yawning Detection for Determining Driver Drowsiness”, Proceeding of IEEE International Workshop on VLSI Design & Video Technology, (2005) May, Suzhou, China
[49]T. Brandt, R. Stemmer, B. Mertsching and A. Rakotonirainy, “Affordable Visual Driver Monitoring System for Fatigue and Monotony”, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, (2004) October, Hague, Netherlands
[50]“B. page and s. correspondent, "road accidents: Sharp rise in fatalities", the daily star, 2019”, https://www.thedailystar.net/backpage/road-accidents-sharp-rise-fatalities-1426999. Accessed: 03 July. 2019
[51]Muhammad Tayeb Khan, Hafeez Anwar, Farhan Ullah, Ata Ur Rehman, Rehmat Ullah, Asif Iqbal, Bok-Hee Lee and Kyung Sup Kwak. “Smart Real-time Video Surveillance Platform for Drowsiness Detection Based on Eyelid Closure”, Wireless Communication and Mobile Computing, Volume 2019, Article ID 2036818, 9 pages, 19 March 2019.