摘要
背景建模是运动目标检测的关键环节,提出了基于改进K均值背景建模的方法,并进行前景提取.该算法在HSV颜色空间对视频流的前N帧中的每个像素样本进行K均值聚类学习,K均值聚类的结果用来表示该像素的背景模型;接着输入的视频流像素与背景模型比较,进行背景、可能前景和阴影的分离,并提出了一种像素相关的选择性背景更新机制;然后利用TOM(Time OutMap)方法来消除鬼影现象.实验结果表明该算法能够很好地对背景进行建模,较精确地提取出运动目标信息,对光照变化具有较强的鲁棒性.
A key issue of detecting moving objects, an approach based on modified K-means to model back- ground is proposed. It learns from the starting N frames with K-means algorithm; and the results learned the background of the pixels. Following, it performs the separation of background pixels, probable foreground pixels and shadow pixels; through the comparison of the input pixels and the background model, a pixel-based selective mechanism of the background update is proposed. Finally, the ghost effects are eliminated by apply- ing the TOM method. The experimental results show that this proposed approach can well model the back- ground, and more accurately extract the moving objects, as well as more robust to the illumination changes.
出处
《三峡大学学报（自然科学版）》
CAS
2012年第6期98-102,共5页
Journal of China Three Gorges University：Natural Sciences
基金
湖北省自然科学基金(2011CDB180)
关键词
K均值聚类
背景减除
运动目标检测
K-means cluster
background subtraction
moving objects detection
