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Fully convolutional crowd counting on highly congested scenes

Marsden, Mark and Little, Suzanne and McGuinness, Kevin and O'Connor, Noel E. (2017) Fully convolutional crowd counting on highly congested scenes. In: 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 27 Feb - 1 Mar 2017, Porto, Portugal. ISBN 978-989-758-226-4

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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, influenced by the work of (Zhang et al., 2016), consists of the following contributions: (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) architecture; (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.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Additional Information:http://www.visapp.visigrapp.org/
Uncontrolled Keywords:Computer Vision; Crowd Counting; Deep Learning
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Multimedia systems
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in:Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP). Proceedings of the International Conference on Computer Vision Theory and Applications 5. SCITEPRESS, Science and Technology Publications. ISBN 978-989-758-226-4
Publisher:SCITEPRESS, Science and Technology Publications
Official URL:https://dx.doi.org/10.5220/0006097300270033
Copyright Information:© 2017 SCITEPRESS
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:21498
Deposited On:10 Mar 2017 12:37 by Kevin McGuinness. Last Modified 05 Jul 2017 13:57

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