Abstract
(Aim) Automatic identification of the car manufacturer in the side-view position can be used for the intelligent traffic monitoring system. Currently, the side-view car recognition did not attract too much attention. (Method) We proposed a novel Ford Motor recognition system. We first captured the car image from the side view. Second, we used wavelet entropy to extract texture features. Third, we employed a back propagation neural network (BPNN) as the classifier. Finally, we employed the Levenberg-Marquardt algorithm to train the classifier. In the experiment, we utilized the 3 × 3-fold cross validation. (Result) This method achieved an overall accuracy of 80% in detecting Ford motors. (Conclusion) This method can detect Ford Motors from the side view effectively. In the future, it may also be used to detect cars of other brands.
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Acknowledgement
This paper is supported by Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025, 15KJB470010), Natural Science Foundation of Jiangsu Province (BK20150983), Natural Science Foundation of China (61602250), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01).
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Jia, WJ., Wang, S., Lu, H., Shao, Y., Lee, E., Zhang, YD. (2017). Ford Motor Side-View Recognition System Based on Wavelet Entropy and Back Propagation Neural Network and Levenberg-Marquardt Algorithm. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_1
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