Earticle

초록

영어

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

목차

Abstract
1. INTRODUCTION
2. RELATED RESEARCH
3. VIDEO ANALYSIS AND EXPERIMENT
3.1 Creating Learning Data
3.2 Pre-processing of Learning Data
3.3 Experiment Method
3.4 Experiment Environment
3.5 Experiment Result
4. CONCLUSION
REFERENCES

키워드

저자

• Jiyoo Lee [ Ph.D. Candidate, Department of IT Convergence , Graduate School, Hansei University, Korea ]
• Seung-Jung Shin [ Professor, Department of ICT Convergence, Hansei University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380