Re-evaluation of the CNN-based state-of-the-art crowd-counting methods with enhancements

Matija Teršek1, Maša Kljun1, Peter Peer1 and Žiga Emeršič1

  1. Faculty of computer and information science
    Večna pot 113, SI-1000 Ljubljana
    {matija.tersek, masa.kljun}@student.uni-lj.si, {peter.peer, ziga.emersic}@fri.uni-lj.si

Abstract

Crowd counting has a range of applications and it is an important task that can help with the accident prevention such as crowd crushes and stampedes in political protests, concerts, sports, and other social events. Many crown counting approaches have been proposed in the recent years. In this paper we compare five deep-learning-based approaches to crowd counting, reevaluate them and present a novel CSRNet-based approach. We base our implementation on five convolutional neural network (CNN) architectures: CSRNet, Bayesian Crowd Counting, DM- Count, SFA-Net, and SGA-Net and present a novel approach by upgrading CSRNet with application of a Bayesian crowd counting loss function and pixel modeling. The models are trained and evaluated on three widely used crowd image datasets, ShanghaiTech part A, part B, and UCF-QNRF. The results show that models based on SFA-Net and DM-Count outperform state-of-the-art when trained and evaluated on the similar data, and the proposed extended model outperforms the base model with the same backbone when trained and evaluated on the significantly different data, suggesting improved robustness levels.

Key words

Crowd counting, convolutional neural networks, deep learning

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS210925023T

Publication information

Volume 19, Issue 3 (September 2022)
Year of Publication: 2022
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Teršek, M., Kljun, M., Peer, P., Emeršič, Ž.: Re-evaluation of the CNN-based state-of-the-art crowd-counting methods with enhancements. Computer Science and Information Systems, Vol. 19, No. 3, 1177-1198. (2022), https://doi.org/10.2298/CSIS210925023T