A Unified Framework for Coarse-to-Fine Recognition of Traffic Signs using Bayesian Network and Visual Attributes

Hamed Habibi Aghdam, Elnaz Jahani Heravi, Domenec Puig

2015

Abstract

Recently, impressive results have been reported for recognizing the traffic signs. Yet, they are still far from the real-world applications. To the best of our knowledge, all methods in the literature have focused on numerical results rather than applicability. First, they are not able to deal with novel inputs such as the false-positive results of the detection module. In other words, if the input of these methods is a non-traffic sign image, they will classify it into one of the traffic sign classes. Second, adding a new sign to the system requires retraining the whole system. In this paper, we propose a coarse-to-fine method using visual attributes that is easily scalable and, importantly, it is able to detect the novel inputs and transfer its knowledge to the newly observed sample. To correct the misclassified attributes, we build a Bayesian network considering the dependency between the attributes and find their most probable explanation using the observations. Experimental results on the benchmark dataset indicates that our method is able to outperform the state-of-art methods and it also possesses three important properties of novelty detection, scalability and providing semantic information.

Paper Citation

in Harvard Style

Habibi Aghdam H., Jahani Heravi E. and Puig D. (2015). A Unified Framework for Coarse-to-Fine Recognition of Traffic Signs using Bayesian Network and Visual Attributes . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 87-96. DOI: 10.5220/0005303500870096

in Bibtex Style

@conference{visapp15,
author={Hamed Habibi Aghdam and Elnaz Jahani Heravi and Domenec Puig},
title={A Unified Framework for Coarse-to-Fine Recognition of Traffic Signs using Bayesian Network and Visual Attributes},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={87-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005303500870096},
isbn={978-989-758-090-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - A Unified Framework for Coarse-to-Fine Recognition of Traffic Signs using Bayesian Network and Visual Attributes
SN - 978-989-758-090-1
AU - Habibi Aghdam H.
AU - Jahani Heravi E.
AU - Puig D.
PY - 2015
SP - 87
EP - 96
DO - 10.5220/0005303500870096