基于分区灰度投影稳像的运动目标检测算法

针对视频监控系统中，复杂环境引起摄像机抖动，造成运动目标检测不准确的问题，提出了一种基于分区灰度投影稳像的运动目标检测算法.首先对每帧图像进行分区，利用分区灰度投影算法对图像各分区的运动矢量进行准确提取和相关性分析，进行抖动判断，并对抖动帧进行运动补偿.然后利用高斯混合背景建模算法进行运动目标提取.最后对目标提取结果进行形态学处理，以进一步提高目标提取的精度.实验结果表明，本文算法较好地消除了场景中运动目标对运动矢量计算的干扰，实现了在摄像机抖动视频场景中的运动目标的准确检测和提取，大大降低了抖动视频目标检测的虚警率.

Moving Targets Detection Based on Subzone Gray Projection Video Stabilization

In video surveillance systems, noise and shake of the background caused by complex environment can greatly influence the detection of moving objects. In order to solve this problem, a Gaussian Mixture Model (GMM) based on subzone gray projection video stabilization algorithm was proposed. First, each frame was divided into several blocks, and the subzone gray projection algorithm was applied to the frame to exactly extract the motion vector of each subzone and analyze the correlation between them. Based on the above analysis, we could decide whether a frame was with dithering or not, and make motion compensation for dithering frame. Then, we used GMM algorithm to extract the moving objects. Finally, morphology was applied as post-processing to further improve the detection accuracy. The subjective and objective evaluations of many different videos were implemented to verify the validity of the proposed algorithm in our experiments. The experiment results have shown that the proposed algorithm can detect the moving targets accurately from the scenes with dithering and suppress the false alarm rate significantly.