PT Unknown
AU Mohamed Ilyes Lakhal
   Hakan Cevikalp
   Sergio Escalera
TI CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification
BT 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
PY 2018
BP 137
EP 144
VL 5
DI 10.5220/0006533601370144
DE Vehicle Classification; Deep Learning; End-to-end Learning
AB 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.
ER