An Indoor Sign Dataset (ISD): An Overview and Baseline Evaluation

João Almeida, Franklin Flores, Max Roecker, Marco Braga, Yandre Costa

Abstract

Visually impaired people need help from others when they need to find specific destinations and cannot guide themselves in indoor environments using signs. Computer Vision Systems can help them with this kind of tasks. In this paper, we present to the research community an Indoor Sign Dataset (ISD), a novel dataset composed of 1,200 samples of indoor signs images labeled into one of the following classes: accessibility, emergency exit, men’s toilets, women’s toilets, wifi and no smoking. The ISD dataset consists of images in different environments conditions, perspectives, and appearance that turns the recognition task quite challenging. A data augmentation technique was applied, generating 69,120 images. We also present baseline results obtained using handcrafted features, like LBP, Color Histogram, HOG, and DAISY applied on SVM, k-NN, and MLP classifiers. We further make non-handcrafted features learned using convolutional neural networks (CNN). The best result was obtained using a CNN model, with an accuracy of 90.33%. This dataset and techniques can be applied to design a wearable device able to help visually impaired people.

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Paper Citation


in Harvard Style

Almeida J., Flores F., Roecker M., Braga M. and Costa Y. (2019). An Indoor Sign Dataset (ISD): An Overview and Baseline Evaluation.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-354-4, pages 505-512. DOI: 10.5220/0007375705050512


in Bibtex Style

@conference{visapp19,
author={João Almeida and Franklin Flores and Max Roecker and Marco Braga and Yandre Costa},
title={An Indoor Sign Dataset (ISD): An Overview and Baseline Evaluation},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2019},
pages={505-512},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007375705050512},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - An Indoor Sign Dataset (ISD): An Overview and Baseline Evaluation
SN - 978-989-758-354-4
AU - Almeida J.
AU - Flores F.
AU - Roecker M.
AU - Braga M.
AU - Costa Y.
PY - 2019
SP - 505
EP - 512
DO - 10.5220/0007375705050512