earticle

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.