摘要
提出一种新的建立背景模型和更新背景的方法。首先使用一种新颖的方法建立初始背景,然后对每一输入帧使用背景差分法获得前景,并利用分类器将前景分类为运动目标、静止目标、虚假目标、噪声,最后在背景更新阶段采用基于前景目标的背景更新方法。实验结果表明,该方法能建立可靠初始背景,并能有效地解决背景更新“死锁”问题,增强背景模型的鲁棒性。
Proposes a novel background modeling and update algorithm. Firstly a novel background modeling is built and for each image sequence foreground is obtained by background subtraction, then a foreground object classifier is used to get moving object, static object, false object, and noise, finally the background model is updated by a foreground object classification based background update algorithm. Extensive experiments results on indoor and outdoor image sequences demonstrate that the proposed system can effectively build a reliable background model and resolve the deadlock problem which results from false object.
出处
《计算机应用研究》
CSCD
北大核心
2006年第5期239-241,共3页
Application Research of Computers
基金
国家"863"计划资助项目(863-306-ZD05)
湖南农业大学人才引进基金资助项目(03YJ08)
关键词
背景模型
前景分类
虚假目标
背景更新
Background Model
Foreground Object Classification
False Object
Background Update
