摘要
针对智能监控中运动目标检测的问题,提出了基于Davinci-DM6467的高斯混合模型像素级的背景分割策略。对彩色图像建立高斯混合模型,根据场景中象素点的稳定性来调整模型参数的更新速率;通过和马氏阈值进行对比来判断是不是要更新背景模型;通过和背景阈值进行对比来判断哪几个模型是属于背景区域。经验证性实验测试,结果表明,高斯混合模型在运动检测中实时性好,对环境有较强的鲁棒性。
In the light of movement target detection during intelligence monitoring, the author put forward Gaussian mixture model of pixel level background segmentation strategy based on Davinei-DM6467. Firstly establishing Gaussian mixture model for colorful images and then adjusting the updating velocity of model parameters according to the stability of each pixels in frames ; secondly comparing them with Markov threshold to judge whether to update the background model; finally comparing them with back-ground threshold to judge which several models belong to background region. Experimental results show that Gaussian mixture model in motion detection is possessed of good real-time and strong robustness to environment.
出处
《阜阳师范学院学报（自然科学版）》
2012年第2期69-72,76,共5页
Journal of Fuyang Normal University(Natural Science)
基金
阜阳师范学院产学研项目(2010CXY04)
阜阳师范学院产学研重点项目(KJ2012A218)
安徽省教育厅自然科学研究项目(KJ2011Z292)
安徽省自然科学基金(090416238)资助
关键词
高斯混合模型
运动目标检测
马氏阈值
背景阈值
gaussian mixture model
movement target detection
markov threshold
background threshold.
