Simultaneous analysis of driver behaviour and road condition for driver distraction detection

International Journal of Image and Data Fusion

Volumn 2, Issue 3, 2011, Pages 217-236

  Rezaei, Mahdi ^a   Klette, Reinhard ^a  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

Cascaded classifier; Driver assistance systems; Driver distraction detection; Face and eye detection; Haar like features

Indexed keywords

EID: 84860424013     PISSN: 19479832     EISSN: 19479824     Source Type: Journal    
DOI: 10.1080/19479832.2011.590458     Document Type: Review

References (41)

1. Bahram, Z., Abedini, A., and Sarshar, M., 2009. Driver assistance system for curvy roads using fuzzy logic. In: Proceedings of the international conference on artificial intelligence, Nevada, 229-235.
2. Batista, J., 2007. A drowsiness and point of attention monitoring system for driver vigilance. In: Proceedings of the IEEE intelligent transportation systems. Seattle, 702-708.
3. Bergasa, L.M., et al., 2006. Real-time system for monitoring driver vigilance. IEEE Transactions on Intelligent Transportation Systems, 7, 63-77.
4. Bishop, R., 2005. Intelligent vehicle technology and trends. Norwood, MA: Artech House Inc.
5. Clanton, J. M., Bevly, D. M., and Hodel, S., 2009. A low-cost solution for an integrated multisensor lane departure warning system. IEEE Transactions on Intelligent Transportation Systems, 10, 47-59.
6. Danescu, R. and Nedevschi, S., 2009. Probabilistic lane tracking in difficult road scenarios using stereo vision. IEEE Transactions on Intelligent Transportation Systems, 10, 272-282.
7. Doshi, A., and Trivedi, M.M., 2009. Head and gaze dynamics in visual attention and context learning. In: Proceedings of the IEEE CVPR joint workshop visual contextual learning visual scene understanding, Miami, FL, 77-84.
8. FERET, 2010. The Facial Recognition Technology Database. Available from: http://www.itl.nist.gov/iad/humanid/feret/feret_master.html [Accessed 12 December 2010].
9. FOT, 2011. Face of Tomorrow. Available from: http://www.faceoftomorrow.com [Accessed 25 January 2011].
10. Freund, Y. and Schapire, R.E., 1999. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14, 771-780.
11. Furman, G. D, et al., 2008. Early detection of falling asleep at the wheel: a heart rate variability approach, Bologna, Computers in Cardiology, 35, 1109-1112.
12. Gandhi, T. and Trivedi, M.M., 2006. Vehicle surround capture: survey of techniques and a novel omni-video-based approach for dynamic panoramic surround maps. IEEE Transactions on Intelligent Transportation Systems, 7, 293-308. IBEO Automative Co. 2010. [online] Available from: http://www.ibeo-as.com/ [Accessed 15 December 2010].
13. Jiang, R., et al., 2009. New lane model and distance transform for lane detection and tracking. In: Proceedings of the computer analysis images patterns, Mü nster, Germany, 1044-1052.
14. Kaempchen, N. and Dietmayer, K., 2003. Data synchronization strategies for multi-sensor fusion. World congress on intelligent transportation systems. Madrid, T2250.
15. Klette, R., et al., 2010. Current challenges in vision-based driver assistance. In: P. Wiederhold and R.P. Barneva, eds. Progress in combinatorial image analysis. Singapore: Research Publ. Services, 3-22.
16. Kol, S., et al., 2006. Road lane departure warning using optimal path finding of the dynamic programming. SICE-ICASE International Joint Conference, 2919-2923.
17. Lin, C. T., et al., 2005. EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Transactions on Circuits System, 52, 2726-2738.
18. Malmivuo, J. and Plonsey, R., 1995. Bioelectromagnetism, principles and applications of bioelectric and biomagnetic fields. New York, Oxford: Oxford University Press.
19. Miyaji, M., et al., 2008. Driver's cognitive distraction detection using AdaBoost on pattern recognition basis. In: Proceedings of the IEEE international conference on vehicular electronics safety, Columbus, OH, 51-56.
20. Miyaji, M., Kawanaka, H., and Oguri, K., 2010. Effect of pattern recognition features on detection for driver's cognitive distraction. In: Proceedings of the international IEEE annual conference on intelligent transportation systems, Madeira Island, Portugal, 605-610.
21. National Traffic Safety Administration (NHTSA), 2009. Available from: http://www.nhtsa.dot.gov/, [Accessed 12 June 2010].
22. Ontario Safety League, 2010. Public Advocacy Report, Road Safety Information.
23. Peiris, M.T.R., et al., 2006a. Detecting behavioral microsleeps from EEG power spectra. IEEE EMBAS annual international conference. New York City, 5723-5725.
24. Peiris, M.T.R., et al., 2006b. Frequent lapses of responsiveness during an extended visuomotor tracking task in non-sleep-deprived subjects. Journal of Sleep Research, 15, 291-300.
25. Portouli, E., et al., 2007. On-road experiment for collecting driving behavioural data of sleepy drivers. Somnologie-Schlafforschung und Schlafmedizin, 11, 259-267.
26. Rezaei, M., Sarshar, M., and Snaatiyan, M., 2010. Toward next generation of driver assistance systems: A multimodal sensor-based platform. In: Proceedings of the international conference on computer automation engineering, Singapore, 62-67.
27. Rezaei, G.M., and Fasih, A., 2007. A hybrid method in driver and multisensor data fusion, using a fuzzy logic supervisor for vehicle intelligence. In: Proceedings of the international conference on sensor technologies applications, Valencia, Spain, 393-39.
28. Rezaei, G.M., Sarshar, M.R., and Sabzevari, R., 2009. Introducing a sensor network for advanced driver assistance systems using fuzzy logic and sensor data fusion techniques. International Journal of Ad Hoc & Sensor Wireless Networks. 8, 35-55.
29. Rezaei, G.M. and Sabzevari, R., 2009. Multisensor data fusion strategies for advanced driver assistance systems. In: N. Milisavljevic, ed. International book of sensor and data Fusion, I-tech education and publishing KG. Vienna, Austria, 141-166.
30. Robert Bosch GmbH, 2010. Available from: http://rb-kwin.bosch.com/en/automotivetechnology/overview/ [Accessed 14 August 2010].
31. Rongben, W., et al., 2004. Monitoring mouth movement for driver fatigue or distraction with One camera. In: Proceedings of the international IEEE conference on intelligent transportation systems. Washington, DC, 314-319.
32. Sigari, M.H., 2009. Driver hypo-vigilance detection based on eyelid behaviour. In: Proceedings of the international conference on advances pattern recognition. Kolkata, 426-429.
33. Velodyne Co., 2011. Available from: http://www.velodyne.com/lidar/lidar.aspx [Accessed 7 January 2011].
34. Velupillai, S. and Güvenç, L., 2009. Laser scanners for driver-assistance systems in intelligent vehicles. IEEE Control Systems Magazine, 29, 17-19.
35. Viola, P. and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Kauai, HI, USA, I511-I518.
36. World Health Organization (WHO), 2010. WHO's global status report on road safety, Department of Violence & Injury Prevention & Disability (VIP).
37. Weigel, H., Lindner, P., and Wanielik, G., 2009. Vehicle tracking with lane assignment by camera and lidar sensor fusion. In: Proceedings of the IEEE intelligent vehicles symposium. Xi'an, 513-520.
38. Wijesoma, W.S., Kodagoda, K.R.S., and Balasuriya, A.P., 2004. Road-boundary detection and tracking using lidar sensing. IEEE Transactions on Robotics Automation, 20, 456-464.
39. Wu, W., Chen, X., and Yang, J., 2005. Detection of text on road signs from Video. IEEE Transactions on Intelligent Transportation System, 6, 378-390.
40. YALE, 2010. Face Database, University of Yale, Available from: http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html [Accessed 24 November 2010].
41. Zhou, Y., et al., 2006. A robust lane detection and tracking method based on computer vision, Measurement Science and Technology, 7, 62-80.