Sistema de Detecção de Fadiga e Desvio de Atenção de Condutores de Veículos
Resumo
Neste trabalho propomos um sistema de detecção de fadiga e distração de condutores. Este sistema é baseado em técnicas de processamento de imagens e reconhecimento de padrões para identificar situações de risco criadas por sonolência e distrações. As imagens são obtidas por uma câmera posicionada em frente ao motorista. O estado de sonolência do condutor é detectado utilizando o PERCLOS. Quando o PERCLOS está acima de um limiar pré-determinado o sistema acusa sonolência e quando a face do motorista não está frontal por um certo tempo o sistema acusa distração. Neste trabalho testamos o sistema em ambiente real com motoristas profissionais. O sistema aqui proposto se mostrou apto a detectar as situações de risco, atingindo 90% de precisão na detecção de distrações, além de fadiga e sonolência.
Referências
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A. Kokonozi, E. Michail, I. Chouvarda, and N. Maglaveras, “A study of heart rate and brain system complexity and their interaction in sleepdeprived subjects,” in Computers in Cardiology, 2008. IEEE, 2008, pp. 969–971.
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A. Eskandarian and A. Mortazavi, “Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection,” in Intelligent Vehicles Symposium, 2007 IEEE. IEEE, 2007, pp. 553–559.
A. Dasgupta, A. George, S. Happy, and A. Routray, “A vision-based system for monitoring the loss of attention in automotive drivers.” IEEE Trans. Intelligent Transportation Systems, vol. 14, no. 4, pp. 1825–1838, 2013.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1. IEEE, 2001, pp. I–I.
J. Yang, D. Zhang, A. F. Frangi, and J.-y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 26, no. 1, pp. 131–137, 2004.
M. Topi, O. Timo, P. Matti, and S. Maricor, “Robust texture classification by subsets of local binary patterns,” in Pattern Recognition, 2000. Proceedings. 15th International Conference on, vol. 3. IEEE, 2000, pp. 935–938.
W. W. Wierwille, S. Wreggit, C. Kirn, L. Ellsworth, and R. Fairbanks, “Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness. final report,” Tech. Rep., 1994.
B.-G. Lee and W.-Y. Chung, “Driver alertness monitoring using fusion of facial features and bio-signals,” IEEE Sensors Journal, vol. 12, no. 7, pp. 2416–2422, 2012.
Z.-L. Zheng and F. Yang, “Enhanced active shape model for facial feature localization,” in Machine Learning and Cybernetics, 2008 International Conference on, vol. 5. IEEE, 2008, pp. 2841–2845.
F. Zhang, J. Su, L. Geng, and Z. Xiao, “Driver fatigue detection based on eye state recognition,” in Machine Vision and Information Technology (CMVIT), International Conference on. IEEE, 2017, pp. 105–110.
Y. Freund, R. Schapire, and N. Abe, “A short introduction to boosting,” Journal-Japanese Society For Artificial Intelligence, vol. 14, no. 771-780, p. 1612, 1999.
R. O. RCCM SANTOS, “Anlise de desempenho de um sistema embarcado de deteco de fadiga de condutores,” IV Simpósio Brasileiro de Engenharia de Sistemas Computacionais, 2014.
R. Lienhart and J. Maydt, “An extended set of haar-like features for rapid object detection,” in Image Processing. 2002. Proceedings. 2002 International Conference on, vol. 1. IEEE, 2002, pp. I–I.
W.-H. Liao, “Region description using extended local ternary patterns,” in Pattern Recognition (ICPR), 2010 20th International Conference on. IEEE, 2010, pp. 1003–1006.
S. A. Ahmed, S. Dey, and K. K. Sarma, “Image texture classification using artificial neural network (ann),” in Emerging Trends and Applications in Computer Science (NCETACS), 2011 2nd National Conference on. IEEE, 2011, pp. 1–4.
A. G. Guggisberg, J. Mathis, U. S. Herrmann, and C. W. Hess, “The functional relationship between yawning and vigilance,” Behavioural brain research, vol. 179, no. 1, pp. 159–166, 2007.
Publicado
06/11/2018
Como Citar
SANTOS, Ricardo C. C. de Meira; OLIVEIRA, Ricardo A. O.; AMORIM, Vicente J. P..
Sistema de Detecção de Fadiga e Desvio de Atenção de Condutores de Veículos. In: TRABALHOS EM ANDAMENTO - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 8. , 2018, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
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ISSN 2763-9002.
