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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Enhancing Surveillance Systems: Integration of Object, Behavior, and Space Information in Captions for Advanced Risk

Minseong Jeon¹, Jaepil Ko², Kyungjoo Cheoi¹

¹Department of Computer Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This study introduces a novel surveillance system using image captioning for risk assessment. The system accurately identifies safety, hazard, and danger levels by analyzing scene descriptions generated by AI.

Keywords:

BERT BLIP-2 descriptive captions image captioning risk assessment surveillance system

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Area of Science:

Computer Vision
Artificial Intelligence
Risk Management

Background:

Traditional surveillance systems lack nuanced risk assessment capabilities.
Integrating AI-driven image analysis can enhance security.
Effective risk evaluation requires understanding scene context.

Purpose of the Study:

To develop a novel surveillance system using image captioning for enhanced risk assessment.
To create a unique dataset for training and evaluating AI models in surveillance.
To accurately categorize risk levels in real-time surveillance scenarios.

Main Methods:

Utilized image captioning to generate descriptive scene narratives.
Developed a custom dataset with [Image-Caption-Danger Score] entries.
Fine-tuned the BLIP-2 model for caption generation.
Employed BERT for caption interpretation and risk level evaluation (1-7 stages).

Main Results:

Achieved high accuracy rates: 92.3% for safety, 89.8% for hazard, and 94.3% for danger.
Demonstrated the effectiveness of AI-powered caption analysis in risk assessment.
Validated the system's capability to interpret complex scene elements including objects, actions, and spatial context.

Conclusions:

The proposed image captioning-based surveillance system offers a significant advancement in risk assessment.
The novel approach provides a comprehensive understanding of surveillance scenes.
This method enhances the accuracy and effectiveness of security monitoring.