基于拉普拉斯分布模型的静止物体检测方法

针对监控视频中静止物体的检测，提出了一种基于拉普拉斯分布模型的检测方法。该方法首先改进[∑-Δ]背景建模方法，快速提取视频背景，构成初级背景，然后在初级背景中引入拉普拉斯分布模型，从而构成精确的自适应动态背景，最后比较初级背景与动态背景之间的差异达到检测静止物体的目的。实验结果表明，该方法能在标准视频数据库中有效地检测到静止行李，并对人群拥挤和光照变化等复杂场景有良好的检测效果。

Static objects detection method based on Laplacian distribution model

In view of static objects detection in surveillance video, this paper proposes a method to solve above problem based on Laplacian distribution model. To begin with, the background modeling procedure of [∑-Δ] method is improved to abstract fast the background of video and form the primary background. And then, Laplacian distribution model is introduced to the primary background so that to abstract video self-adaptive dynamic background accurately which can reflect the background of video better. At last, the absolute difference between the primary background and dynamic background is used to detect the static objects. Experimental results prove the validity of this method to detect static luggage based on video standard databases and this method is robust in some complex scenes including crowded and illumination change.