Scene invariant crowd counting

Ryan, David, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton B. (2011) Scene invariant crowd counting. In Digital Image Computing : Technqiues and Applications 2011, 6-8 December 2011, Sheraton Noosa Resort & Spa, Noosa, QLD.

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This paper describes a scene invariant crowd counting algorithm that uses local features to monitor crowd size. Unlike previous algorithms that require each camera to be trained separately, the proposed method uses camera calibration to scale between viewpoints, allowing a system to be trained and tested on different scenes. A pre-trained system could therefore be used as a turn-key solution for crowd counting across a wide range of environments. The use of local features allows the proposed algorithm to calculate local occupancy statistics, and Gaussian process regression is used to scale to conditions which are unseen in the training data, also providing confidence intervals for the crowd size estimate. A new crowd counting database is introduced to the computer vision community to enable a wider evaluation over multiple scenes, and the proposed algorithm is tested on seven datasets to demonstrate scene invariance and high accuracy. To the authors' knowledge this is the first system of its kind due to its ability to scale between different scenes and viewpoints.

Impact and interest:

8 citations in Scopus
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Full-text downloads:

259 since deposited on 18 Sep 2011
20 in the past twelve months

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ID Code: 46038
Item Type: Conference Paper
Refereed: Yes
Additional Information: This paper will be published in "Digital Image Computing: Technqiues and Applications, 2011. DICTA '11. Proceedings"
Keywords: crowd counting, scene invariant, local features, density estimation, crowd monitoring
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2011 [please consult the author]
Deposited On: 18 Sep 2011 22:16
Last Modified: 22 Jun 2015 15:51

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