Abstract
To extend the functionalities of Advanced Driver Assistance Systems (ADAS) and have a more accurate control on the parameters of sensors mounted on an intelligent vehicle, a tool that can classify the scenarios which the vehicle moves in, is needed.
This article presents a comparison of three classification techniques (PCA, ANN and SVM) to obtain a fast and robust scene classifier based only on images. The systems presented in this paper have been trained on three different categories of traffic scenarios: urban, highway, and rural, on a total of more than 23 hours of driving in different countries.
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Bernini, N., Bertozzi, M., Devincenzi, L., Mazzei, L. (2013). Comparison of Three Approaches for Scenario Classification for the Automotive Field. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_59
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DOI: https://doi.org/10.1007/978-3-642-41181-6_59
Publisher Name: Springer, Berlin, Heidelberg
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