Traffic monitoring is one of the most popular applications of automated video surveillance. Classification of the vehicles into types is important in order to provide the human traffic controllers with updated information about the characteristics of the traffic flow, which facilitates their decision making process. In this work, a video surveillance system is proposed to carry out such classification. First of all, a feature extraction process is carried out to obtain the most significant features of the detected vehicles. After that, a set of Growing Neural Gas neural networks is employed to determine their types. A qualitative and quantitative assessment of the proposal is carried out on a set of benchmark traffic video sequences, with favorable results.
CITATION STYLE
Molina-Cabello, M. A., Luque-Baena, R. M., López-Rubio, E., Ortiz-De-lazcano-lobato, J. M., Domínguez, E., & Pérez, J. M. (2017). Vehicle classification in traffic environments using the growing neural gas. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10306 LNCS, pp. 225–234). Springer Verlag. https://doi.org/10.1007/978-3-319-59147-6_20
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