A Visual-Based Driver Distraction Recognition and Detection Using Random Forest
Driver distraction and fatigue are considered the main cause of most car accidents today. This paper compares the performance of Random Forest and a number of other well-known classifiers for driver distraction detection and recognition problems. A non-intrusive system, which consists of hardware components for capturing the driver’s driving sessions on a car simulator, using infrared and Kinect cameras, combined with a software component for monitoring some visual behaviors that reflect a driver’s level of distraction, was used in this work.
In this system, five visual cues were calculated: arm position, eye closure, eye gaze, facial expressions, and orientation. These cues were then fed into a classifier, such as AdaBoost, Hidden Markov Models, Random Forest, Support Vector Machine, Conditional Random Field, or Neural Network, in order to detect and recognize the type of distraction. The use of various cues resulted in a more robust and accurate detection and classification of distraction, than using only one. The system was tested with various sequences recorded from different users. Experimental results were very promising, and show the superiority of the Random Forest classifier compared to the other classifiers.
KeywordsSupport Vector Machine Hide Markov Model Conditional Random Field Intelligent Transportation System Object Distraction
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- 1.Distracted driving, http://www.cdc.gov/Motorvehiclesafety/Distracted_Driving/index.html
- 2.Microsoft kinect face tracking, http://msdn.microsoft.com/en-us/library/jj130970.aspx
- 4.Butakov, V., Ioannou, P., Tippelhofer, M., Camhi, J.: Driver/vehicle response diagnostic system for vehicle following based on gaussian mixture model. In: 2012 IEEE 51st Annual Conference on Decision and Control (CDC), pp. 5649–5654. IEEE (2012)Google Scholar
- 7.Holahan, C.J.: Relationship between roadside signs and traffic accidents: A field investigation, Research Report 54. Council for Advanced Transportation Studies, Austin, TX (1977)Google Scholar
- 9.Murphy-Chutorian, E., Doshi, A., Trivedi, M.M.: Head pose estimation for driver assistance systems: A robust algorithm and experimental evaluation. In: IEEE Intelligent Transportation Systems Conference, ITSC 2007, pp. 709–714. IEEE (2007)Google Scholar
- 10.Pohl, J., Birk, W., Westervall, L.: A driver-distraction-based lane-keeping assistance system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221(4), 541–552 (2007)Google Scholar
- 13.Torkkola, K., Massey, N., Wood, C.: Driver inattention detection through intelligent analysis of readily available sensors. In: Proceedings of the The 7th International IEEE Conference on Intelligent Transportation Systems, pp. 326–331. IEEE (2004)Google Scholar