%0 Conference Proceedings
%T CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification
%A Mohamed Ilyes Lakhal
%A Hakan Cevikalp
%A Sergio Escalera
%B 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
%D 2018
%V 5
%F Mohamed Ilyes Lakhal2018
%O HUPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3094), last updated on Fri, 21 Jan 2022 14:39:26 +0100
%X Vehicle type classification is considered to be a central part of Intelligent Traffic Systems. In the recent years, deep learning methods have emerged in as being the state-of-the-art in many computer vision tasks. In this paper, we present a novel yet simple deep learning framework for the vehicle type classification problem. We propose an end-to-end trainable system, that combines convolution neural network for feature extraction and recurrent neural network as a classifier. The recurrent network structure is used to handle various types of feature inputs, and at the same time allows to produce a single or a set of class predictions. In order to assess the effectiveness of our solution, we have conducted a set of experiments in two public datasets, obtaining state of the art results. In addition, we also report results on the newly released MIO-TCD dataset.
%K Vehicle Classification
%K Deep Learning
%K End-to-end Learning
%U http://refbase.cvc.uab.es/files/LCE2018a.pdf
%U http://dx.doi.org/10.5220/0006533601370144
%P 137-144