Synthetic Data Generation for Deep Learning in Counting Pedestrians

Hadi Keivan Ekbatani, Oriol Pujol, Santi Segui

2017

Abstract

One of the main limitations of the application of Deep Learning (DL) algorithms is when dealing with problems with small data. One workaround to this issue is the use of synthetic data generators. In this framework, we explore the benefits of synthetic data generation as a surrogate for the lack of large data when applying DL algorithms. In this paper, we propose a problem of learning to count the number of pedestrians using synthetic images as a substitute for real images. To this end, we introduce an algorithm to create synthetic images for being fed to a designed Deep Convolutional Neural Network (DCNN) to learn from. The model is capable of accurately counting the number of individuals in a real scene.

Paper Citation

in Harvard Style

Keivan Ekbatani H., Pujol O. and Segui S. (2017). Synthetic Data Generation for Deep Learning in Counting Pedestrians . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 318-323. DOI: 10.5220/0006119203180323

in Bibtex Style

@conference{icpram17,
author={Hadi Keivan Ekbatani and Oriol Pujol and Santi Segui},
title={Synthetic Data Generation for Deep Learning in Counting Pedestrians},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={318-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006119203180323},
isbn={978-989-758-222-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Synthetic Data Generation for Deep Learning in Counting Pedestrians
SN - 978-989-758-222-6
AU - Keivan Ekbatani H.
AU - Pujol O.
AU - Segui S.
PY - 2017
SP - 318
EP - 323
DO - 10.5220/0006119203180323