Abstract
In this paper a moving vehicle detection algorithm based on visual processing mechanism with multiple pathways is proposed, in which the multiple pathways visual processing mechanism is inspired by the biological visual system. According to the different moving directions of front vehicles, orientation selectivity of visual cortex cells is used to construct a visual processing model with three pathways. In each pathway, an AdaBoost cascade classifier is trained using a set of special samples for detection of moving vehicles. The AdaBoost cascade classifier is response to multi-block local binary patterns (MB-LBP) of vehicles. The experimental results show that the multiple pathways visual processing mechanism, compared with the single pathway AdaBoost cascade classifier and the conventional method, not only can reduce the complexity of the classifier and training time, but also can improve the recognition rate of moving vehicle.
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References
Nicholls, J.G., Martin, A.R., Wallace, B.G.: From Neuron to Brain, 4th edn. Sinauer Associates Inc., Sunderland (2001)
Nieuwenhuys, R., Huijzen, C.V., Voogd, J.: The Human Central Nervous System. A Synopsis and Atlas. Springer, Heidelberg (1979)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)
Yeul-Min, B., Whoi-Yul, K.: Forward vehicle detection using cluster-based AdaBoost. Opt. Eng. 53(10), 1021103 (2014)
Cai, Y.H.: Fusing multiple features to detect on-road vehicles. Comput. Technol. Autom. 32(1), 98–102 (2013)
Jin, L.S., Wang, Y., Liu, J.H., Wang, Y.L., Zheng, Y.: Front vehicle detection based on Adaboost algorithm in daytime. J. Jilin Univ. (Engineering and Technology Edition) 44(6), 1604–1608 (2014)
Mita, T., Kaneko, T., Hori, O.: Joint haar-like features for face detection. In: ICCV (2005)
Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)
Wu, Q.X., McGinnity, T.M., Maguire, L.P., Belatreche, A., Glackin, B.: Processing visual stimuli using hierarchical spiking neural networks. Neurocomputing 71(10-12), 2055–2068 (2008)
Wu, Q.X., McGinnity, T.M., Maguire, L.P., Cai, R.T., Chen, M.G.: A visual attention model based on hierarchical spiking neural networks. Neurocomputing 116, 3–12 (2013)
Acknowledgments
The authors gratefully acknowledge supports from Fujian Provincial Key Laboratory for Photonics Technology, and the fund from the Natural Science Foundation of China (Grant No. 61179011) and Science and Technology Major Projects for Industry-academic Cooperation of Universities in Fujian Province (Grant No. 2013H6008), and supports from Innovation Team of the Ministry of Education (IRT1115).
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Chen, Y., Wu, Q., Xie, H., Hong, S., Li, X. (2015). Moving Vehicle Detection Based on Visual Processing Mechanism with Multiple Pathways. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_27
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DOI: https://doi.org/10.1007/978-3-319-22053-6_27
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