Title: Safety monitoring system for tourist scenic spots based on crowd scene type recognition

Authors: Qinqin Dong

Addresses: Business School, Xinyang University, Xinyang, 464000, China

Abstract: The tourism industry's rapid growth necessitates addressing the safety of tourist attractions. This study proposes a safety monitoring system based on crowd scene recognition. Using a dual-channel deep convolutional neural network model, the system combines static and dynamic features to identify and monitor potential risks in crowded areas. A comprehensive security monitoring solution is provided by integrating crowd distribution analysis and crowd type recognition using motion feature maps. The proposed density field-based crowd distribution analysis demonstrated superior estimation accuracy across various datasets. The safety monitoring system achieved an average accuracy of 82.35% and 81.32% in heterogeneous and homogeneous crowd scenes, respectively, with average area under the curve of 79.12% and 75.38%. These results significantly outperformed the comparison algorithms, confirming the study's effectiveness. The objective is to design accurate algorithms capable of estimating crowd types and behaviour patterns in complex scenarios, thereby enhancing the safety management and tourism experience at attractions.

Keywords: crowd scene type recognition; tourist attractions; safety monitoring; density field; deep convolutional neural network.

DOI: 10.1504/IJSN.2024.141780

International Journal of Security and Networks, 2024 Vol.19 No.3, pp.128 - 137

Received: 22 Dec 2023
Accepted: 06 Jul 2024
Published online: 01 Oct 2024 *

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