Abstract
Vehicle-to-vehicle distance calculation has a great significance to driving assistance and estimation of traffic condition. In this paper, we present an on-board video-based method about calculating distance gap. The method is mainly divided into three major stages. At first stage, an Adaboost cascade classifier using Haar-like features of sample pictures is used to detect preceding vehicles. At second stage, a fusion algorithm combining Maximally Stable Extremal Regions (MSER) for far vehicles with vertical texture method for close vehicles is applied to locate license plate. At the third stage, distance gap is calculated according to the pixel height of plate and the proportion of plate pixel height. Experimental results in this paper showed excellent performance of the method in calculating distance gap.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China [61273006, 61573030], and Open Project of Beijing Key Laboratory of Urban Road Intelligent Traffic Control [XN070], Scientific Research Foundation of Hebei Higher Education [QN2015209], Science and Technology Support Foundation of Hebei Province [13210807].
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Liu, Y., Chen, Y., Ren, J., Xin, L. (2017). Calculating Vehicle-to-Vehicle Distance Based on License Plate Detection . In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-319-38789-5_57
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DOI: https://doi.org/10.1007/978-3-319-38789-5_57
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