research-article

Arbitrary perspective crowd counting via local to global algorithm

Authors:

Teng LiAuthors Info & Claims

Multimedia Tools and Applications, Volume 79, Issue 21-22

Pages 15059 - 15071

https://doi.org/10.1007/s11042-020-08888-5

Published: 01 June 2020 Publication History

Abstract

Crowd counting is getting more and more attention. More and more collective activities, such as the Olympics Games and the World Expo, are also important to control the crowd number. In this paper, we address the problem of crowd counting in the crowded scene. Our model accurately estimated the count of people in the crowded scene. Firstly, we proposed a novel and simple convolutional neural network, called Global Counting CNN (GCCNN). The GCCNN can learn a mapping, transforms the appearance of image patches to estimated density maps. Secondly, the Local to Global counting CNN (LGCCNN), calculating the density map from local to global. Stiching the local patches constrains the final density map of the larger area, which makes up for the difference values in the perspective map. In general, it makes the final density map more accurate. The dataset we used is a set of public dataset, which are WorldExpo’10 dataset, Shanghaitech dataset, the UCF_CC_50 dataset and the UCSD dataset. The experiments have proved our method achieves the state-of-the-art result over other algorithms.

References

[1]

Chan AB and Vasconcelos NCounting people with low-level features and bayesian regressionIEEE Trans Image Process20122142160-21772959518

[2]

Cong Z, Li H, Wang X, Yang X (2015) Cross-scene crowd counting via deep convolutional neural networks. In: IEEE conference on computer vision & pattern recognition

[3]

Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Schmid C, Soatto S, Tomasi C (eds) International conference on computer vision & pattern recognition (CVPR ’05). 10.1109/CVPR.2005.177. https://hal.inria.fr/inria-00548512, vol 1. IEEE Computer Society, San Diego, pp 886–893

[4]

Ge W, Collins RT (2009) Marked point processes for crowd counting. In: IEEE conference on computer vision & pattern recognition

[5]

He K, Zhang X, Ren S, Jian S (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification

[6]

Hu Y, Chang H, Nian F, Yan W, and Teng L Dense crowd counting from still images with convolutional neural networks J Vis Commun Image Represent 2016 38 C 530-539

[7]

Ke C, Chen CL, Gong S, Tao X (2012) Feature mining for localised crowd counting. In: British machine vision conference

[8]

Lempitsky VS, Zisserman A (2010) Learning to count objects in images. In: International conference on neural information processing systems

[9]

Lin SF, Chen JY, and Chao HX Estimation of number of people in crowded scenes using perspective transformation Systems Man & Cybernetics Part A Systems & Humans IEEE Transactions on 2001 31 6 645-654

[10]

Liu R, Chen Y, Zhu X, and Hou K Image classification using label constrained sparse coding Multimed Tools Appl 2016 75 23 15619-15633

[11]

Liu T, Tao D (2014) On the robustness and generalization of cauchy regression. In: IEEE international conference on information science & technology

[12]

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

[13]

Lowe DG, Lowe D (1999) Object recognition from local scale-invariant features. In: Proc. iccv

[14]

Loy CC, Gong S, Xiang T (2014) From semi-supervised to transfer counting of crowds. In: IEEE international conference on computer vision

[15]

Min L, Zhang Z, Huang K, Tan T (2009) Estimating the number of people in crowd- ed scenes by mid based foreground segmentation and head-shoulder detection. In: International conference on pattern recognition

[16]

Ojala T, Pietikinen M, and Menp T Gray scale and rotation invariant texture classification with local binary patterns IEEE Trans Pattern Anal Mach Intell 2002 24 7 971-987

[17]

Rodriguez M, Laptev I, Sivic J, Audibert JY (2011) Density-aware person detection and tracking in crowds. In: International conference on computer vision

[18]

Sam DB, Surya S, Babu RV (2017) Switching convolutional neural network for crowd counting

[19]

Shen Z, Xu Y, Ni B, Wang M, Hu J, Yang X (2018) Crowd counting via adver- sarial cross-scale consistency pursuit. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5245–5254

[20]

Sindagi VA, Patel VM (2017) Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE international conference on computer vision, pp 1861–1870

[21]

Wu B, Nevatia R (2005) Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Tenth IEEE international conference on computer vision

[22]

Zhang Y, Zhou D, Chen S, Gao S, Yi M (2016) Single-image crowd counting via multi-column convolutional neural network. In: Computer vision & pattern recognition

Cited By

Liu MWang YYi HHuang X(2024)Vehicle object counting network based on feature pyramid split attention mechanismThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02808-y40:2(663-680)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00371-023-02808-y
Xiong LYi HHuang XHuang W(2023)An efficient multi-scale contextual feature fusion network for counting crowds with varying densities and scalesMultimedia Tools and Applications10.1007/s11042-022-13920-x82:9(13929-13949)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13920-x
Wang YLiu MYi HXie YTan ZJi C(2022)High-density Image Object Counting Network Based on Spatial Context and Channel AttentionProceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering10.1145/3573428.3573708(1592-1596)Online publication date: 21-Oct-2022
https://dl.acm.org/doi/10.1145/3573428.3573708

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Arbitrary perspective crowd counting via local to global algorithm
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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 79, Issue 21-22

Jun 2020

1437 pages

ISSN:1380-7501

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2020

Accepted: 27 March 2020

Revision received: 16 March 2020

Received: 31 March 2019

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the National Key R&D Program of China
the Anhui Provincial Natural Science Foundation of China

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Cited By

Liu MWang YYi HHuang X(2024)Vehicle object counting network based on feature pyramid split attention mechanismThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02808-y40:2(663-680)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00371-023-02808-y
Xiong LYi HHuang XHuang W(2023)An efficient multi-scale contextual feature fusion network for counting crowds with varying densities and scalesMultimedia Tools and Applications10.1007/s11042-022-13920-x82:9(13929-13949)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13920-x
Wang YLiu MYi HXie YTan ZJi C(2022)High-density Image Object Counting Network Based on Spatial Context and Channel AttentionProceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering10.1145/3573428.3573708(1592-1596)Online publication date: 21-Oct-2022
https://dl.acm.org/doi/10.1145/3573428.3573708

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