Warning Zone Abnormal Behavior Detection: Pre-Alarms for Risk of Exhibit Damage

In recent years, the vandalism of art exhibits has become a significant issue in museums and exhibitions. To address this problem, we propose an automated module system : Geo-Fence Discriminator (GFD), Behavior Surveillance (BS), and Behavior Response Alarm System (BRAS)—that predicts visitor behavior and ensures the safety of exhibits through preemptive warnings. The proposed system includes the GFD module, which determines safe and risky distances from the exhibits based on the coordinates of objects. For behavior recognition, the BS module collects a behavior dataset and classifies and identifies actions through trained image classification. Detected abnormal behavior information is then transmitted in real-time to BRAS, which displays alerts. This enables real-time prediction of abnormal visitor behavior near the exhibits and sends warnings to exhibition staff, thereby enhancing the protection of the artworks.

Authors

Institutions

Publication Details

Published
2024-08-12
DOI
https://doi.org/10.1109/metacom62920.2024.00062
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Warning Zone Abnormal Behavior Detection: Pre-Alarms for Risk of Exhibit Damage

Jaeho Kim, Dasol Kim, Dong-Hoon Lee
Anomaly Detection Techniques and Applications
article

Warning Zone Abnormal Behavior Detection: Pre-Alarms for Risk of Exhibit Damage

Jaeho Kim, Dasol Kim, Dong-Hoon Lee
article en

Abstract

In recent years, the vandalism of art exhibits has become a significant issue in museums and exhibitions. To address this problem, we propose an automated module system : Geo-Fence Discriminator (GFD), Behavior Surveillance (BS), and Behavior Response Alarm System (BRAS)—that predicts visitor behavior and ensures the safety of exhibits through preemptive warnings. The proposed system includes the GFD module, which determines safe and risky distances from the exhibits based on the coordinates of objects. For behavior recognition, the BS module collects a behavior dataset and classifies and identifies actions through trained image classification. Detected abnormal behavior information is then transmitted in real-time to BRAS, which displays alerts. This enables real-time prediction of abnormal visitor behavior near the exhibits and sends warnings to exhibition staff, thereby enhancing the protection of the artworks.

Sejong University (KR)
Climate action
Openalex Percentile: Top 82%
Anomaly Detection Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.