Journal of Applied Science and Engineering

Published by Tamkang University Press

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Real-time Detection of Abnormal Pedestrian Behavior in Scenic Areas Using Hybrid Gaussian Models and Regional Feature Analysis

 2026

 Volume 33

Ping Xu1 and Aziguli Abudouwaili2

1Shanxi Vocational University of Culture and Tourism

2Moray (Beijing) Data Technology Co., Ltd.

Received: March 14, 2026
Accepted: April 29, 2026
Publication Date: July 18, 2026

上傳圖片

Results of a partial experiment 

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The rapid development of the tourism industry has made the need for managing tourists’ behaviors in scenic spots more urgent. The irregularities of the tourists, such as unauthorized climbing, writing graffiti, and entering the forbidden regions, can cause security hazards and negatively impact the reputation of the location. In this paper, a novel framework is introduced for recognizing abnormal behaviors of pedestrians in the region of interest using the characteristics extracted from surveillance videos. To achieve a robust background subtraction, a mixture of Gaussians is applied, and then the bounding boxes of motion objects are computed using minimum bounding rectangles. To recognize periodic changes in aspect ratios, curve fitting is employed to find abnormal behaviors.

Keywords: Abnormal behavior detection; video surveillance; hybrid Gaussian model; regional features; real-time monitoring.

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