摘要
传统混合高斯模型一般为每个像素分配固定的高斯分布个数,从而造成背景形成速度的减慢和系统资源的浪费;同时也存在着高斯模型背景建模中的缓慢或滞留运动物体造成目标误判现象的问题(即空洞问题)。为此,提出了一种有效的两阶段视频图像处理方法。该方法在第一阶段根据像素点的优先级大小自动地调节高斯分布的数目,在第二阶段首先对像素点进行所属区域的划分,进而对目标区域和非目标区域采取不同的更新手段。实验表明,采用两阶段视频图像处理方法明显地改善了背景建模的速度,有效解决了提取目标出现的空洞问题。
The traditional mixture Gaussian model always allocates a fixed number of Gaussian distribution for each pixel. Thus, it causes the slowdown of the background germinate speed and waste of system resource Meanwhile, it exists that the object misjudge phenomenon, namely empty problem, caused by slowdown or demurrage of moving object in the modeling of Gaussian model background. To solve these problems, the paper proposes an effective two-phase method for video images processing. In the first phase, this method automatically adjusts the number of Gaussian distribution based on the priority of each pixel. In the second phase, it firstly partitions the area of each pixel, then uses different update method for object area and non-object area. The experiments show that the two-phase method for video image processing can significantly improve the speed of background model. Furthermore, it can efficiently resolve the empty problem in extracting objects.
出处
《微型机与应用》
2011年第11期31-33,36,共4页
Microcomputer & Its Applications
基金
国家自然科学基金资助(60772317)
陕西省自然科学基础研究计划资助(2006F30)
关键词
背景建模
混合高斯模型
背景更新
目标检测
background model
mixture Gaussian model
background update
object detection
