Journal of Intelligent Systems (Nov 2024)
Detection of abnormal tourist behavior in scenic spots based on optimized Gaussian model for background modeling
Abstract
In recent years, tourist attractions have become increasingly popular vacation destinations, leading to a gradual emergence of the tourism market and an improvement in the regulatory systems of scenic spots. However, these attractions frequently experience various abnormal behaviors (ABs). The existing abnormal behavior detection (ABD) algorithms are hindered by interference from the scenic area background, resulting in poor identification of ABs. The study proposed a background model constructed by an optimized Gaussian mixture model based on the background subtraction method to eliminate the background interference. Based on this model, an ABD method was established using action data based on spatio-temporal block detection and motion foreground effect map features. Experiments were conducted to train the video clips and action databases to establish the ABD model. In the study, algorithms were tested using computers, and different scenic spots were set up to validate tourists. Three scenic spot scenes with different time and environmental conditions were set up to detect tourist ABD, and the results were compared with different existing anomaly detection algorithms. Research has shown that the area under the receiver operation characteristic curve in outdoor scenes a and c is significantly larger than that in indoor scene b, with areas under the curves of 96.55 and 97.40%, respectively. In two outdoor research scenarios b and c, the area under the curve and equal error rate of the research algorithm (RA) are 88.14, 18.24, 98.12, and 7.55%, respectively, which are significantly better than the two compared algorithms. Although they are 97.31 and 6.29% in scenario a, slightly lower than the 95.18 and 12.69% of the compared algorithms, the expected time is very close to the ideal value, and overall, they have good performance. In addition, the average recognition accuracy of the RA (93.24%) is higher than that of the comparative algorithm (89.32%). The proposed ABD method has shown certain efficiency and accuracy in identifying ABs. It can provide effective decision-making support for scenic area management and has reference significance for improving the safety management quality of scenic area monitoring. By monitoring the AB of tourists, scenic area management personnel can take timely measures to ensure the safety of tourists and the order of the scenic area. In addition, the results of this study not only provide a new approach and method for detecting AB of scenic spot tourists, but also provide valuable reference significance and theoretical contributions in fields such as video surveillance, behavior analysis, and artificial intelligence.
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