Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Development of Checking System for Emergency using Behavior-based Object Detection

행동기반 사물 감지를 통한 위급상황 확인 시스템 개발

  • Kim, MinJe (Dept. of Smart Information and Communication Engineering, Sangmyung University) ;
  • Koh, KyuHan (Dept. of Computer Science, California State University Stanislaus) ;
  • Jo, JaeChoon (Division of Computer Engineering, Hanshin University)
  • 김민제 (상명대학교 스마트 정보통신공학과) ;
  • 고규한 (캘리포니아 주립대학교 컴퓨터학과) ;
  • 조재춘 (한신대학교 컴퓨터공학부)
  • Received : 2020.04.27
  • Accepted : 2020.06.20
  • Published : 2020.06.28
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Abstract

Since the current crime prevention systems have a standard mechanism that victims request for help by themselves or ask for help from a third party nearby, it is difficult to obtain appropriate help in situations where a prompt response is not possible. In this study, we proposed and developed an automatic rescue request model and system using Deep Learning and OpenCV. This study is based on the prerequisite that immediate and precise threat detection is essential to ensure the user's safety. We validated and verified that the system identified by more than 99% of the object's accuracy to ensure the user's safety, and it took only three seconds to complete all necessary algorithms. We plan to collect various types of threats and a large amount of data to reinforce the system's capabilities so that the system can recognize and deal with all dangerous situations, including various threats and unpredictable cases.

기존의 방범 시스템은 피해자가 직접 구조를 요청하거나 인근 제 3자에 의해 도움을 받아야 하는 구조이기 때문에 신속하게 대응이 불가능한 상황에서는 경우에 따라 적절한 도움을 받기 힘들다. 본 연구에서는 Deep Learning과 OpenCV를 활용한 자동 구조 요청 모델을 제안하고 시스템을 개발하였다. 본 연구는 사용자의 안전을 보장할 수 있어야 하기 때문에 신속히 정확한 결과를 도출할 수 있어야 한다는 전제 조건이 밑바탕 되어 객체의 정확성은 약 99% 이상을 확인할 수 있었으며 모든 알고리즘이 종료되는 데까지의 소요 시간을 약 3초까지 단축시킬 수 있었다. 다양한 위협 요소와 예측 불가능한 특수한 경우 등 모든 위험 상황을 인식하기 위해 다양한 종류의 위협 요소와 많은 양의 데이터를 수집하여 예기치 못한 상황에도 대처할 수 있도록 강화하여야 할 것이다.

Keywords

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