Development of CCTV Contextualization Based Environment of Disorder Detection Using Deep Learning

• Eunbi Cho,
• JeongHyeon Chang,
• Kwanghoon Pio Kim,

Vol. 19, No. 3, March 31, 2025
10.3837/tiis.2025.03.018, Download Paper (Free):

• Disorder
• YOLO
• BLIP
• Image captioning
• Deep Learning
• caption generation
• video analysis
• Contextualization

Abstract

This study presents a novel, automated approach for the detection of physical and social disorder using contextualized video data from CCTV footage. By leveraging deep learning techniques, this method enhances the precision and scalability of urban disorder surveillance. Traditional methods, such as Systematic Social Observation (SSO) and surveys, face inherent limitations, including observer bias and scalability issues, which reduce their effectiveness in large-scale or long-term applications. To overcome these challenges, this research implements deep learning models, specifically YOLO and BLIP, to extract a wide range of contextual information from video frames, improving the accuracy and scalability of detecting social and physical disorder. The extracted data is categorized using a pre-defined disorder word dictionary, allowing for structured and systematic analysis. The proposed model automates the entire detection process, eliminating the need for human intervention and significantly reducing the time and costs associated with traditional methods. Additionally, the model transforms large-scale video data into structured text, facilitating efficient storage and retrieval. By calculating spatial distances between individuals and hazardous objects, the model also enables the identification of complex social disorder signs that are difficult to detect with conventional video analysis techniques.

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