Abstract
The following article is dedicated to techniques for recognition of vehicles on the road. By using 3D virtual models of vehicles, it is possible to create database of point cloud. The SSCD algorithm for training and testing was used. First for each 3D model the point clouds were created. Then from each point cloud one hundred pictures were rendered from different projections. Creation of filtered dataset was done by selection six angles from these projections. This dataset contains 100 models of vehicles divided into 5 classes. In summary, final non-filtered dataset contains 10 000 pictures, filtered dataset consist of 600 pictures. Dataset was used in support vector machine (SVM) and convolutional neural network (CNN) for training and testing in ratio 80:20. The result for SVM was 40%, this was done because non-filtered dataset contains many similar projections. Moreover, the size resulted in long duration of experiment (<90 h). Therefore, other experiments were done with filtered dataset. In filtered dataset, best result in SVM was 79% with RBF kernel. For the next experiment, CNN was used. With data augmentation the result was 80%, without 89%.
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The paper was prepared under the support of the ERDF European Regional Development Fund, project No. ITMS26220120028, “Centre of excellence for systems and services of intelligent transport”.
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Kamencay, P., Hudec, R., Orjesek, R., Sykora, P. (2018). Vehicles Recognition Based on Point Cloud Representation. In: Kováčiková, T., Buzna, Ľ., Pourhashem, G., Lugano, G., Cornet, Y., Lugano, N. (eds) Intelligent Transport Systems – From Research and Development to the Market Uptake. INTSYS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 222. Springer, Cham. https://doi.org/10.1007/978-3-319-93710-6_9
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