An evaluation of crowd counting methods, features and regression models
Ryan, David, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton B. (2015) An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding, 130, pp. 1-17.
Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression
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|Item Type:||Journal Article|
|Keywords:||Crowd counting, Holistic features, Local features, Histogram features, Regression|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 Elsevier Inc.|
|Copyright Statement:||This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, [in press (2014)] DOI: 10.1016/j.cviu.2014.07.008|
|Deposited On:||01 Sep 2014 22:04|
|Last Modified:||16 Jul 2015 05:04|
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