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Fully convolutional crowd counting on highly congested scenes |
Marsden, Mark; Little, Suzanne; McGuinness, Kevin; O'Connor, Noel E.
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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.
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Keyword(s):
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Machine learning; Artificial intelligence; Multimedia systems; Image processing; Computer Vision; Crowd Counting; Deep Learning |
Publication Date:
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2017 |
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Type:
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Conference item |
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Peer-Reviewed:
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Yes |
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Language(s):
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English |
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Institution:
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Dublin City University |
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Funder(s):
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Science Foundation Ireland |
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Citation(s):
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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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Publisher(s):
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SCITEPRESS, Science and Technology Publications |
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File Format(s):
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application/pdf |
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Related Link(s):
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http://doras.dcu.ie/21498/1/VISAPP_crowd_counting_mark_2017.pdf |
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First Indexed:
2017-03-11 05:54:52 Last Updated:
2017-07-09 05:07:52 |
