Earticle

초록

영어

Kidnapping is a crime that can have disastrous results for the victim and their family. It is important to develop effective systems to detect and prevent such incidients. This paper proposes a Kidnapping Detection Systems that uses Real-Time Object Detection YOLO-v7, and Skeleton Extraction module AlphaPose to detect and track potential kidnapping event in real-time. The system utilizes a number of surveillance cameras that are already installed in Korea. It employs surveillance camera system as an edge module and a GPU system as a server module. By performing deep detection only when there is a high likelihood of a kidnapping event at the edge device, we can reduce inference costs. We have also built a dataset by recording simulated kidnapping scenarios, which can serve as a substitute for actual kidnapping events. Based on our dataset, we achieved an accuracy of 90.3% on the test set using a rule-based approach that considers the angle of the legs and occlusion with people and a car. Our system shows a promising solution for enhancing public safety and preventing crimes.

목차

Abstract
1. Introduction
2. Related works
2.1 YOLO
2.2 AlphaPose
3. Methods
3.1. Dataset
3.2. Model Architecture
3.3. Experiment setup
4. Experiment result
5. Conclusions
Acknowledgement
References

저자

• 이가원 [ 인공지능학과 서강대학교 ]
• 박운상 [ 인공지능학과 서강대학교 ] Correspondence author

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