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Machine Learning Techniques for Crowd Counting: A Survey

by Dakshi Chavan, Anuradha Purohit
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 37
Year of Publication: 2023
Authors: Dakshi Chavan, Anuradha Purohit
10.5120/ijca2023923176

Dakshi Chavan, Anuradha Purohit . Machine Learning Techniques for Crowd Counting: A Survey. International Journal of Computer Applications. 185, 37 ( Oct 2023), 1-8. DOI=10.5120/ijca2023923176

@article{ 10.5120/ijca2023923176,
author = { Dakshi Chavan, Anuradha Purohit },
title = { Machine Learning Techniques for Crowd Counting: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2023 },
volume = { 185 },
number = { 37 },
month = { Oct },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number37/32928-2023923176/ },
doi = { 10.5120/ijca2023923176 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:59.883756+05:30
%A Dakshi Chavan
%A Anuradha Purohit
%T Machine Learning Techniques for Crowd Counting: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 37
%P 1-8
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Crowd Counting process estimates the number of people in an image or video. It is a significant area of computer vision research that has numerous applications in crowd management, class student attendance management, temple crowd management, event planning, urban development, security and retail analytic and many more. Due to the increasing interest to provide efficient crowd management and public safety, several researchers have proposed methods based on detection, regression and density. This has made it feasible for both machine learning (ML) and deep learning (DL) approaches to deal with challenges to get accurate crowd counts. Machine learning and deep learning based models identify complex patterns, obtained increased accuracy and adjust to changing environmental conditions efficiently. In this paper, a survey on work done in crowd counting using machine learning techniques has been presented. The advantages and disadvantages of each approach has been discussed in detail.

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Index Terms

Computer Science
Information Sciences

Keywords

Crowd counting Crowd management Machine learning (ML) Deep learning (DL) Detection

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