Authors:
Mohamed Ilyes Lakhal
1
;
Sergio Escalera
2
and
Hakan Cevikalp
3
Affiliations:
1
Queen Mary University of London, United Kingdom
;
2
University of Barcelona and Computer Vision Center UAB, Spain
;
3
Eskişehir Osmangazi University, Turkey
Keyword(s):
Vehicle Classification, Deep Learning, End-to-end Learning.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Early and Biologically-Inspired Vision
;
Features Extraction
;
Image and Video Analysis
;
Media Watermarking and Security
Abstract:
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.