Pedestrian Counting Using Deep Models Trained on Synthetically Generated Images

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Ghosh S, Amon P, Hutter A, Kaup A
Publication year: 2017
Pages range: 86-97
ISBN: 978-989-758-226-4
Language: English


Abstract

Counting pedestrians in surveillance applications is a common scenario. However, it is often challenging to
obtain sufficient annotated training data, especially so for creating models using deep learning which require
a large amount of training data. To address this problem, this paper explores the possibility of training a deep
convolutional neural network (CNN) entirely from synthetically generated images for the purpose of counting
pedestrians. Nuances of transfer learning are exploited to train models from a base model trained for image
classification. A direct approach and a hierarchical approach are used during training to enhance the capability
of the model for counting higher number of pedestrians. The trained models are then tested on natural images
of completely different scenes captured by different acquisition systems not experienced by the model during
training. Furthermore, the effectiveness of the cross entropy cost function and the squared error cost function
are evaluated and analyzed for the scenario where a model is trained entirely using synthetic images. The
performance of the trained model for the test images from the target site can be improved by fine-tuning using
the image of the background of the target site.


FAU Authors / FAU Editors

Ghosh, Sanjukta
Lehrstuhl für Multimediakommunikation und Signalverarbeitung


How to cite

APA:
Ghosh, S., Amon, P., Hutter, A., & Kaup, A. (2017). Pedestrian Counting Using Deep Models Trained on Synthetically Generated Images. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) (pp. 86-97). Porto, Portugal, PT.

MLA:
Ghosh, Sanjukta, et al. "Pedestrian Counting Using Deep Models Trained on Synthetically Generated Images." Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), Porto, Portugal 2017. 86-97.

BibTeX: 

Last updated on 2019-15-04 at 16:38