东南大学学报（英文版）2004，Vol.20Issue(4)：423-426,4.
基于噪声与阴影抑制多模态背景模型的运动物体检测
Multimodal background model with noise and shadow suppression for moving object detection
摘要
Abstract
A statistical multimodal background model is described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects, and shadows suppression is provided. The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity. Pixel's neighbor information is considered to remove noise due to camera jitter and small motion in the scene. The hue-max-min-diff color information is used to detect and suppress moving cast shadows. The effectiveness of the proposed method in the foreground segmentation is demonstrated in the traffic surveillance application.关键词
视频监视/背景模型/核密度估计/阴影抑制/HMMD色彩空间Key words
video surveillance/background model/kernel density estimation/shadow suppression/hue-max-min-diff (HMMD) color space分类
信息技术与安全科学引用本文复制引用
毛燕芬,施鹏飞..基于噪声与阴影抑制多模态背景模型的运动物体检测[J].东南大学学报（英文版）,2004,20(4):423-426,4.基金项目
The National Basic Research Program of China (973 Program) (No. TG1998030408). （973 Program）
