This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Study of Driver's Driving Concentration Based on Computer Vision Technology
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Driving safety is an eternal theme of the transportation industry. In recent years, with the rapid growth of car ownership, traffic accidents have become more frequent, and the harm it brings to human society has become increasingly serious. In this context, car safety assisted driving technology has received widespread attention. As an effective means to reduce traffic accidents and reduce accident losses, it has become the research frontier in the field of traffic engineering and represents the trend of future vehicle development. However, there are still many technical problems that need to be solved. With the continuous development of computer vision technology, face detection technology has become more and more mature, and applications have become more and more extensive. This article will use the face detection technology to detect the driver's face, and then analyze the changes in driver's driving focus. Firstly, the problem of detecting the eyes and mouth status of the driver is discussed. The purpose is to capture the driver's long-term closed eyes and yawning and other actions closely related to the dozing behavior. Secondly, the problem of estimating the driver's head posture is studied. The purpose is to capture the abnormal movements of the driver's long bow, head up or frequent nodding. The study consists of three parts: detection of facial feature points, estimation of the head posture based on the feature points, and definition of fatigue characteristics. The experimental results show that the method in this paper is not only easy to operate but also has a high accuracy rate for the detection of driver concentration.
CitationLin, G., Zhan, Z., Peng, X., Xu, H. et al., "A Study of Driver's Driving Concentration Based on Computer Vision Technology," SAE Technical Paper 2020-01-0572, 2020, https://doi.org/10.4271/2020-01-0572.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Loukas, D. and Constantinos, A. , “Road Safety Data Considerations,” Accident Analysis & Prevention, 2018, S0001457518302975, doi:10.1016/j.aap.2018.06.019.
- China Communications Yearbook, Ministry of Transport of China, 2001-2011.
- Beirness, D.J., Simpson, H.M., and Desmond, K. , The Road Safety Monitor 2004-Drowsy Driving (Traffic Injury Research Foundation, 2004).
- Power, J.A. , “Sleep Alert Device,” USA, WO2006121512(A2), 2006-11-16.
- Lal, S.K., Craig, A., Boord, P., Kirkup, L., and Nguyen, H. , “Development of an Algorithm for an EEG-Based Driver Fatigue Countermeasure,” Journal of Safety Research 34(3):321-328, 2003, doi:10.1016/S0022-4375(03)00027-6.
- Kim, Y.S., Lee, H.B., Kim, J.S., Baek, H.J., Ryu, M.S., and Park, K.S. , “ECG, EOG Detection from Helmet Based System,” in 2007 6th International Special Topic Conference on Information Technology Applications in Biomedicine, IEEE, 2007, 10.1109/ITAB.2007.4407378.
- Daptardar, S., Lakshminarayanan, V., Reddy, S., Nair, S., Sahoo, S., and Sinha, P. , “Hidden Markov Model Based Driving Event Detection and Driver Profiling from Mobile Inertial Sensor Data,” in 2015 IEEE Sensors, IEEE, 2015, doi:10.1109/ICSENS.2015.7370312.
- Darshana, S., Fernando, D., Jayawardena, S., Wickramanayake, S., and DeSilva, C. , “Efficient PERCLOS and Gaze Measurement Methodologies to Estimate Driver Attention in Real Time,” in 2014 5th International Conference on Intelligent Systems, Modelling and Simulation, IEEE, 2014, doi:10.1109/ISMS.2014.56.
- Saradadevi, M. and Bajaj, P. , “Driver Fatigue Detection Using Mouth and Yawning Analysis,” International Journal of Computer Science and Network Security 8(6):183-188, 2008.
- Omidyeganeh, M., Shirmohammadi, S., Abtahi, S., Khurshid, A. et al. , “Yawning Detection Using Embedded Smart Cameras,” IEEE Transactions on Instrumentation and Measurement 65(3):570-582, 2016, doi:10.1109/TIM.2015.2507378.
- Kong, S.G. and Mbouna, R.O. , “Head Pose Estimation from a 2D Face Image Using 3D Face Morphing with Depth Parameters,” IEEE Transactions on Image Processing 24(6):1801-1808, 2015, doi:10.1109/TIP.2015.2405483.
- Borghi, G., Venturelli, M., Vezzani, R., and Cucchiara, R. , “Poseidon: Face-from-Depth for Driver Pose Estimation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, doi:10.1109/CVPR.2017.583.
- Chuang, M.C., Bala, R., Bernal, E.A., Paul, P., and Burry, A. , “Estimating Gaze Direction of Vehicle Drivers Using a Smartphone Camera,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014, doi:10.1109/CVPRW.2014.30.
- Lienhart, R. and Maydt, J. , “An Extended Set of Haar-Like Features for Rapid Object Detection,” in Proceedings International Conference on Image Processing, IEEE, Vol. 1, 2002, doi:10.1109/ICIP.2002.1038171.
- Wang, F. and Li, Y. , “Beyond Physical Connections: Tree Models in Human Pose Estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, 10.1109/CVPR.2013.83.
- Ahonen, T., Hadid, A., and Pietikainen, M. , “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12):2037-2041, 2006, doi:10.1109/TPAMI.2006.244.
- Xiong, X., and Fernando, D.L.T. , “[IEEE 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Portland, OR, USA (2013.06.23-2013.06.28)] 2013 IEEE Conference on Computer Vision and Pattern Recognition - Supervised Descent Method and Its Applications to Face Alignment,” in 2013 IEEE Conference on Computer Vision and Pattern Recognition IEEE Computer Society, 2013, 532-539, doi:10.1109/CVPR.2013.75.
- Wickramanayake, S., Fernando, D., Jayawardena, S., Darshana, S., Wickramanayake, S. et al. , “Efficient PERCLOS and Gaze Measurement Methodologies to Estimate Driver Attention in Real Time,” in 5th International Conference on Intelligent Systems, Modeling and Simulation(ISMS) IEEE, 2014, 10.1109/ISMS.2014.56.
- Zhang, Z. , “A Flexible New Technique for Camera Calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11):1330-1334, 2000, doi:10.1109/34.888718.
- Luo, S. , “Research on All-Weather Fatigue Driving Detection and Early Warning System Based on Facial Features,” Fuzhou University Master's Thesis, China, Diss., 2014.
- Xu, C. , Research on Some Key Issues of Automobile Assisted Driving Based on Computer Vision (Doctoral dissertation: University of Science and Technology of China, 2009).
- Sumit, J. and Busso, C. , “[IEEE 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) - Rio de Janeiro, Brazil (2016.11.1-2016.11.4)] 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) - Analyzing the Relationship between Head Pose and Gaze to Model Driver Visual Attention,” in IEEE International Conference on Intelligent Transportation Systems IEEE, 2016, 2157-2162, doi:10.1109/ITSC.2016.7795905.
- Carter, C.J. and Laya, O. , “Driver's Visual Search in a Field Situation and in a Driving Simulator,” Vision in Vehicles 6:21-31, 1998.
- Cheng, B., Chuan, M., and Wei, Z. , “Study on the Monitoring Technology of Driver Attention State Based on Machine Vision,” Automotive Engineering 12:1137-1140, doi:10.3321/j.issn:1000-680X.2009.12.009.
- Wierwille, W.W., Antin, J.F., Dingus, T.A., and Hulse, M.C. , “Visual Attentional Demand of an In-car Navigation Display System,” Vision in Vehicles II Second International Conference on Vision in Vehicles, 1988.
- Rockwell, T.H. , “Spare Visual Capacity in Driving-Revisited: New Empirical Results for an Old Idea,” in Vision in Vehicles II. Second International Conference on Vision in Vehicles Applied Vision Association Ergonomics Society Association of Optometrists, 1988.