LMS algorithm을 이용한 배경분리 알고리즘 구현 및 성능 비교에 관한 연구
A study on implementation of background subtraction algorithm using LMS algorithm and performance comparative analysis
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초록
최근 정보화 및 컴퓨터 비전 기술의 발전과 함께 객체의 인식 및 추적 기능을 가진 CCTV시스템이 다양한 분야에서 연구되고 있다. 하지만 실외환경에서 발생할 수 있는 그림자의 변화, 조명의 변화, 움직이는 요소들과 같은 배경의변화는 객체 인지성능에 영향을 주게 된다. 따라서 실외환경에서 배경의 변화를 실시간으로 갱신하기 위해 본 논문에서는 다양한 배경 모델링 기법들을 분석하고, 가중치를 기반으로 한 배경 갱신 알고리즘을 제안한다. 실험을 통해 제안한알고리즘의 객체 검출 성능은 이전 연구의 객체 검출 성능을 유지하며, 오인식 된 객체 수가 이전 연구에 비해 감소됨을 확인하였다.
Abstract
Recently, with the rapid advancement in information and computer vision technology, a CCTV system using object recognitionand tracking has been studied in a variety of fields. However, it is difficult to recognize a precise object outdoors dueto varying pixel values by moving background elements such as shadows, lighting change, and moving elements of the scene. Inorder to adapt the background outdoors, this paper presents to analyze a variety of background models and proposed backgroundupdate algorithms based on the weight factor. The experimental results show that the accuracy of object detection is maintained,and the number of misrecognized objects are reduced compared to previous study by using the proposed algorithm.
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