A Pattern Recognition System for Detecting Use of Mobile Phones While Driving
Rafael A. Berri, Alexandre G. Silva, Rafael S. Parpinelli, Elaine Girardi, Rangel Arthur
2014
Abstract
It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone), containing frontal images of the driver. Support Vector Machine (SVM) with Polynomial kernel is the most advantageous classification system to the features provided by the algorithm, obtaining a success rate of 91.57% for the vision system. Tests done on videos show that it is possible to use the image datasets for training classifiers in real situations. Periods of 3 seconds were correctly classified at 87.43% of cases.
References
- Balbinot, A. B., Zaro, M. A., and Timm, M. I. (2011). Func¸o˜es psicológicas e cognitivas presentes no ato de dirigir e sua importaˆncia para os motoristas no traˆnsito. Cieˆncias e Cognic¸ a˜o/Science and Cognition, 16(2).
- Dadgostar, F. and Sarrafzadeh, A. (2006). An adaptive real-time skin detector based on hue thresholding: A 6It allows you to develop applications integrated with
- the vehicle (available in http://openxcplatform.com) comparison on two motion tracking methods. Pattern Recognition Letters, 27(12):1342-1352.
- Drews, F. A., Pasupathi, M., and Strayer, D. L. (2008). Passenger and cell phone conversations in simulated driving. Journal of Experimental Psychology: Applied, 14(4):392.
- Enriquez, I. J. G., Bonilla, M. N. I., and Cortes, J. M. R. (2009). Segmentacion de rostro por color de la piel aplicado a deteccion de somnolencia en el conductor. Congreso Nacional de Ingenieria Electronica del Golfo CONAGOLFO, pages 67-72.
- Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, 1 edition.
- Goodman, M., Benel, D., Lerner, N., Wierwille, W., Tijerina, L., and Bents, F. (1997). An investigation of the safety implications of wireless communications in vehicles. US Dept. of Transportation, National Highway Transportation Safety Administration.
- Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and Schölkopf, B. (1998). Support vector machines. Intelligent Systems and their Applications, IEEE, 13(4):18-28.
- Hu, M. K. (1962). Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, 8(2):179-187.
- Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In International joint Conference on artificial intelligence, volume 14, pages 1137-1145. Lawrence Erlbaum Associates Ltd.
- NHTSA (2011). Driver electronic device use in 2010. Traffic Safety Facts - December 2011, pages 1-8.
- Peissner, M., Doebler, V., and Metze, F. (2011). Can voice interaction help reducing the level of distraction and prevent accidents?
- Regan, M. A., Lee, J. D., and Young, K. L. (2008). Driver distraction: Theory, effects, and mitigation. CRC.
- Smith, J. R. and Chang, S. F. (1996). Tools and techniques for color image retrieval. In SPIE proceedings, volume 2670, pages 1630-1639.
- Stanimirova, I., Ü stün, B., Cajka, T., Riddelova, K., Hajslova, J., Buydens, L., and Walczak, B. (2010). Tracing the geographical origin of honeys based on volatile compounds profiles assessment using pattern recognition techniques. Food Chemistry, 118(1):171-176.
- Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J., Medeiros-Ward, N., and Biondi, F. (2013). Measuring cognitive distraction in the automobile. AAA Foundation for Traffic Safety - June 2013, pages 1-34.
- Strayer, D. L., Watson, J. M., and Drews, F. A. (2011). 2 cognitive distraction while multitasking in the automobile. Psychology of Learning and MotivationAdvances in Research and Theory, 54:29.
- Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York.
- Veeraraghavan, H., Bird, N., Atev, S., and Papanikolopoulos, N. (2007). Classifiers for driver activity monitoring. Transportation Research Part C: Emerging Technologies, 15(1):51-67.
- Viola, P. and Jones, M. (2001). Robust real-time object detection. International Journal of Computer Vision, 57(2):137-154.
- Wang, L. (2005). Support Vector Machines: theory and applications, volume 177. Springer, Berlin, Germany.
- Watkins, M. L., Amaya, I. A., Keller, P. E., Hughes, M. A., and Beck, E. D. (2011). Autonomous detection of distracted driving by cell phone. In Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, pages 1960-1965. IEEE.
- Yang, J., Sidhom, S., Chandrasekaran, G., Vu, T., Liu, H., Cecan, N., Chen, Y., Gruteser, M., and Martin, R. P. (2011). Detecting driver phone use leveraging car speakers. In Proceedings of the 17th annual international conference on Mobile computing and networking, pages 97-108. ACM.
Paper Citation
in Harvard Style
A. Berri R., G. Silva A., S. Parpinelli R., Girardi E. and Arthur R. (2014). A Pattern Recognition System for Detecting Use of Mobile Phones While Driving . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 411-418. DOI: 10.5220/0004684504110418
in Bibtex Style
@conference{visapp14,
author={Rafael A. Berri and Alexandre G. Silva and Rafael S. Parpinelli and Elaine Girardi and Rangel Arthur},
title={A Pattern Recognition System for Detecting Use of Mobile Phones While Driving},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={411-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004684504110418},
isbn={978-989-758-004-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - A Pattern Recognition System for Detecting Use of Mobile Phones While Driving
SN - 978-989-758-004-8
AU - A. Berri R.
AU - G. Silva A.
AU - S. Parpinelli R.
AU - Girardi E.
AU - Arthur R.
PY - 2014
SP - 411
EP - 418
DO - 10.5220/0004684504110418