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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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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