Vehicle Type Detection by Convolutional Neural Networks - Molina-Cabello, Miguel Ángel; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel; Thurnhofer-Hemsi, Karl (Springer, 2017)
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.
Keywords: Redes neuronales (Informática); Foreground detection; Background modeling; Convolutional neural networks; Probabilistic self-organizing maps; Background features