Synthetic Data Generation for Deep Learning in Counting Pedestrians

Hadi Keivan Ekbatani, Oriol Pujol, Santi Segui

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.

References

  1. Cappelli, R., Erol, A., Maio, D., and Maltoni, D. (2000). Synthetic fingerprint-image generation. In Pattern Recognition, 2000. Proceedings. 15th International Conference on. IEEE.
  2. Chan, A. B., Liang, Z.-S. J., and Vasconcelos, N. (2008). Privacy preserving crowd monitoring: Counting people without people models or tracking. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE.
  3. Chan, A. B., Morrow, M., and Vasconcelos, N. (2009). Analysis of crowded scenes using holistic properties. In Performance Evaluation of Tracking and Surveillance workshop at CVPR.
  4. Ciregan, D., Meier, U., and Schmidhuber, J. (2012). Multicolumn deep neural networks for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE.
  5. Eggert, C., Winschel, A., and Lienhart, R. (2015). On the benefit of synthetic data for company logo detection. In Proceedings of the 23rd ACM international conference on Multimedia. ACM.
  6. Griffin, G., Holub, A., and Perona, P. (2007). Caltech-256 object category dataset. California Institute of Technology.
  7. Kong, D., Gray, D., and Tao, H. (2005). Counting pedestrians in crowds using viewpoint invariant training. In BMVC. Citeseer.
  8. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems.
  9. LeCun, Y. and Bengio, Y. (2005). Convolutional networks for images, speech, and time series. In BMVC. Citeseer.
  10. Leibe, B., Schindler, K., and Van Gool, L. (2007). Coupled detection and trajectory estimation for multi-object tracking. In 2007 IEEE 11th International Conference on Computer Vision. IEEE.
  11. Mahadevan, V., Li, W., Bhalodia, V., and Vasconcelos, N. (2010). Anomaly detection in crowded scenes. In CVPR.
  12. Marana, A., Costa, L. d. F., Lotufo, R., and Velastin, S. (1998). On the efficacy of texture analysis for crowd monitoring. In Computer Graphics, Image Processing, and Vision, 1998. Proceedings. SIBGRAPI'98. International Symposium on. IEEE.
  13. Phua, C., Lee, V., Smith, K., and Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. In arXiv preprint arXiv:1009.6119.
  14. Rabaud, V. and Belongie (2006). Counting crowded moving objects. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE.
  15. Seguí, S., Pujol, O., and Vitria, J. (2015). Learning to count with deep object features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.
  16. Song, H. A. and Lee, S.-Y. (2013). Hierarchical representation using nmf. In International Conference on Neural Information Processing. Springer.
  17. Subramanian, S., Ozaltin, E., and Finlay, J. E. (2011). Height of nations: a socioeconomic analysis of cohort differences and patterns among women in 54 low-to middle-income countries. In PLoS One. Public Library of Science.
  18. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  19. Wu, B. and Nevatia, R. (2005). Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE.
  20. Yao, W., Basu, S., Wei-Nchih, L., and Singhal, S. (2013). Synthetic healthcare data generation. Google Patents.
Download


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