Open Access

ARTICLE

Deep Learning Based Efficient Crowd Counting System

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Muhammad Hamza Faheem², Syed Shah Amanullah Quadri³

1 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 12372, Saudi Arabia
2 Centre of Excellence in Cybercrime and Digital Forensics, Naif Arab University for Security Sciences, Riyadh, 14812, Saudi Arabia
3 Center of Excellence for Information Assurance (COEIA), King Saud University, Riyadh, 12372, Saudi Arabia

* Corresponding Author: Emad Ul Haq Qazi. Email:

(This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)

Computers, Materials & Continua 2024, 79(3), 4001-4020. https://doi.org/10.32604/cmc.2024.048208

Received 30 November 2023; Accepted 08 March 2024; Issue published 20 June 2024

Abstract

Estimation of crowd count is becoming crucial nowadays, as it can help in security surveillance, crowd monitoring, and management for different events. It is challenging to determine the approximate crowd size from an image of the crowd’s density. Therefore in this research study, we proposed a multi-headed convolutional neural network architecture-based model for crowd counting, where we divided our proposed model into two main components: (i) the convolutional neural network, which extracts the feature across the whole image that is given to it as an input, and (ii) the multi-headed layers, which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd. We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance. To analyze the results, we used two metrics Mean Absolute Error (MAE) and Mean Square Error (MSE), and compared the results of the proposed systems with the state-of-art models of crowd counting. The results show the superiority of the proposed system.

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Keywords

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