2026-05-31T04:58:40Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/141142026-02-03T12:06:46Zcom_10630_2254col_10630_37959

Vehicle Type Detection by Convolutional Neural Networks Molina-Cabello, Miguel Ángel Luque-Baena, Rafael Marcos López-Rubio, Ezequiel Thurnhofer-Hemsi, Karl Redes neuronales (Informática) In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations. 2017-07-05T10:54:50Z 2017-07-05T10:54:50Z 2017-07-05T10:54:50Z 2017 conference output J.M. Ferrández Vicente et al. (Eds.): IWINAC 2017, Part II, LNCS 10338, pp. 268–278, 2017. DOI: 10.1007/978-3-319-59773-728 http://hdl.handle.net/10630/14114 http://orcid.org/0000-0001-8231-5687 eng International Work-Conference on the Interplay between Natural and Artificial Computation 2017 La Coruña Junio 2017 open access by-nc-nd Springer