Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios

Rita Delussu, Lorenzo Putzu, Giorgio Fumera

2020

Abstract

Crowd counting and density estimation are crucial functionalities in intelligent video surveillance systems but are also very challenging computer vision tasks in scenarios characterised by dense crowds, due to scale and perspective variations, overlapping and occlusions. Regression-based crowd counting models are used for dense crowd scenes, where pedestrian detection is infeasible. We focus on real-world, cross-scene application scenarios where no manually annotated images of the target scene are available for training regression models, but only images with different backgrounds and camera views can be used (e.g., from publicly available data sets), which can lead to low accuracy. To overcome this issue, we propose to build the training set using synthetic images of the target scene, which can be automatically annotated with no manual effort. This work provides a preliminary empirical evaluation of the effectiveness of the above solution. To this aim, we carry out experiments using real data sets as the target scenes (testing set) and using different kinds of synthetically generated crowd images of the target scenes as training data. Our results show that synthetic training images can be effective, provided that also their background, beside their perspective, closely reproduces the one of the target scene.

Paper Citation

in Harvard Style

Delussu R., Putzu L. and Fumera G. (2020). Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 365-372. DOI: 10.5220/0008981803650372

in Bibtex Style

@conference{visapp20,
author={Rita Delussu and Lorenzo Putzu and Giorgio Fumera},
title={Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={365-372},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008981803650372},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios
SN - 978-989-758-402-2
AU - Delussu R.
AU - Putzu L.
AU - Fumera G.
PY - 2020
SP - 365
EP - 372
DO - 10.5220/0008981803650372
PB - SciTePress