Non-parametric Background Generation based on MRF Framework

Abstract

ABSTRACT Previous background generation techniques showed bad performance in complex environments since they used only temporal contexts. To overcome this problem, in this paper, we propose a new background generation method which incorporates spatial as well as temporal contexts of the image. This enabled us to obtain ‘clean’ background image with no moving objects. In our proposed method, first we divided the sampled frame into m*n blocks in the video sequence and classified each block as either static or non-static. For blocks which are classified as non-static, we used MRF framework to model them in temporal and spatial contexts. MRF framework provides a convenient and consistent way of modeling context-dependent entities such as image pixels and correlated features. Experimental results show that our proposed method is more efficient than the traditional one.Keywords:Background Generation, Background Model, Surveillance System, MRF Framework, Object Tracking 1. 서 론 1) 비디오 감시 시스템이나 모니터링 시스템과 같이 다양한 컴퓨터 비전 응용분야에서 물체 검출과 추적은 매우 중요한 요소로서 현재도 활발한 연구가 이루어지고 있는 분야이다. 많은 검출 및 추적 시스템에서 움직이는 물체가 포함되어 있지 않은 배경 영상은 물체 검출 및 추적을 위한 참조 정보로서 이용된다. 하지만 대부분의 경우, 이러한 배경 영상을 획득하는 것은 어려운 일이다.