Fully Convolutional Crowd Counting on Highly Congested Scenes

Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O’Connor

Abstract

In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016). Producing an accurate and robust crowd count estimator using computer vision techniques has attracted significant research interest in recent years. Applications for crowd counting systems exist in many diverse areas including city planning, retail, and of course general public safety. Developing a highly generalised counting model that can be deployed in any surveillance scenario with any camera perspective is the key objective for research in this area. Techniques developed in the past have generally performed poorly in highly congested scenes with several thousands of people in frame (Rodriguez et al., 2011). Our approach, based largely on the work of (Zhang et al., 2016), consists of the following steps: (1) A training set augmentation scheme that minimises redundancy among training samples to improve model generalisation and overall counting performance; (2) a deep, single column, fully convolutional network (FCN); (3) a multi-scale averaging step during inference. The developed technique can analyse images of any resolution or aspect ratio and achieves state-of-the-art counting performance on the Shanghaitech Part_B and UCF_CC_50 datasets as well as competitive performance on Shanghaitech Part_A.

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Paper Citation


in Harvard Style

Marsden M., McGuinness K., Little S. and E. O’Connor N. (2017). Fully Convolutional Crowd Counting on Highly Congested Scenes . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 27-33. DOI: 10.5220/0006097300270033


in Bibtex Style

@conference{visapp17,
author={Mark Marsden and Kevin McGuinness and Suzanne Little and Noel E. O’Connor},
title={Fully Convolutional Crowd Counting on Highly Congested Scenes},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={27-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006097300270033},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Fully Convolutional Crowd Counting on Highly Congested Scenes
SN - 978-989-758-226-4
AU - Marsden M.
AU - McGuinness K.
AU - Little S.
AU - E. O’Connor N.
PY - 2017
SP - 27
EP - 33
DO - 10.5220/0006097300270033