基于形态学的高斯模型和八邻域帧差法混合运动目标检测算法

针对视频中运动目标的提取问题，提出一种基于形态学的高斯模型和八邻域帧差法相融合的提取算法。该算法首先将视频中某些帧转化为灰度图，建立以混合高斯分布为基础的统计模型，并结合八邻域帧差法提取出运动目标的大致轮廓，然后利用自适应更新的高斯模型算法进行精确的减除，最后再进行形态学处理，从而使检测出的运动目标更加清晰完整。实验结果表明，该算法对含有低速运动物体、阴影较多的视频提取效果较好，具有很好的鲁棒性。

#br# A Moving Target Algorithm Based on Gaussian Mixture Model #br# and Eight-neighbor FDM with Morphological Processing

For the extraction of the moving subjects in a video clip, this paper proposes a moving target detection algorithm based on improved Gaussian mixture model and eight-neighbor frame difference method. Firstly, some frames of a video are converted into grayscal images and a statistics model based on Gaussian mixture distribution is set up. Following up, the sketch of the moving part is acquired through eight-neighbor frame difference method. For a more precise subtraction, the Gaussian mixture model is added to the algorithm. Combining with morphological processing, a complete and precise foreground is extracted. Experimental results show that the proposed algorithm has better effect on videos with slow-moving objects and large shadow areas, compared to earlier algorithms, and it is robust in multiple occasions.