摘要
复杂场景的背景建模、运动目标检测、运动目标所投射阴影的检测与抑制在智能监控、机器人视觉、视频会议等领域有着广泛的应用。在运动前景检测阶段,给出了一种改进的混合高斯算法进行场景的背景建模,根据各点像素值出现的混乱程度采取不同的高斯函数参数更新机制,缓解了混合高斯算法计算量大的问题。在运动目标的阴影检测与抑制中,提出了一种基于混合高斯的阴影抑制算法,该算法先利用阴影在HSV颜色空间的特点,判断被检测为运动前景的像素是否为疑似阴影,然后用混合高斯阴影模型对所有疑似阴影值进行聚类,进一步完成阴影抑制。仿真结果表明:该算法可更有效地抑制阴影对运动目标检测的影响,并具有较强的实时性。
The background modeling of complex environment, moving object detection, and moving cast shadow suppression could be applied to a lot of fields such as intelligent surveillance, robot vision and videoeonference ect. In moving foreground detection, an improved mixture Gaussian-based background modeling method was presented, which updated the parameters of Gaussians according to the frequency of pixel value changes, to reduce the cost of computation. In shadow suppression, a mixture Gaussian-based clustering algorithm was provided to detect and suppress shadow. This method firstly identifies whether a pixel value is probable shadow by shadow model in HSV color space, the pixel values detected as probable shadow are then put into mixture Gaussian shadow model to learn and cluster. The experimental results indicates that the oroDosed aooroach in this paper can process in real-time and remove shadow more effectively.
出处
《计算机应用》
CSCD
北大核心
2006年第5期1021-1023,1026,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(60372085)
陕西省科学技术研究发展计划资助项目(2003K06G15)
关键词
背景建模
混合高斯
阴影抑制
HSV颜色空间
background modeling
Gaussian mixture
shadow suppression
HSV color space
