Crowd Counting and People Density Detection: An Overview

DOI

10.2991/978-94-6463-447-1_46How to use a DOI?

Keywords

Crowd Counting; People Density Detection; Convolutional Neural Networks; Training and Optimization Methods; YOLO

Abstract

Crowd counting and crowd density estimation are important tasks in the field of computer vision, involving the detection of people in images or videos and the estimation of crowd density. In this domain, deep learning algorithms such as Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO) play crucial roles. CNNs are specialized deep learning models for processing image data. They extract features and learn representations from images through multiple convolutional layers, pooling layers, and fully connected layers. CNNs have achieved remarkable success in tasks such as image classification, object detection, and semantic segmentation, providing effective solutions for problems like head counting and crowd density estimation. On the other hand, YOLO is a fast and accurate object detection algorithm. Its unique feature is the ability to predict multiple bounding boxes and their corresponding class probabilities in a single forward pass. YOLO divides the image into smaller grid cells and performs bounding box and class predictions in each cell, enabling efficient object detection. In summary, crowd counting and crowd density estimation present significant challenges in computer vision. With the help of deep learning algorithms like CNNs and YOLO, researchers can address these challenges more accurately, providing powerful technical support for applications in crowd management, security surveillance, and other fields.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fuqiang Jin
AU  - Zhaoguo Zhang
AU  - Yi Ning
AU  - Yi Lu
AU  - Wei Song
AU  - Xingguo Qin
AU  - Jinlong Chen
PY  - 2024
DA  - 2024/07/14
TI  - Crowd Counting and People Density Detection: An Overview
BT  - Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)
PB  - Atlantis Press
SP  - 434
EP  - 454
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-447-1_46
DO  - 10.2991/978-94-6463-447-1_46
ID  - Jin2024
ER  -