自动分割视频序列中运动物体的新算法：多特征联合方法

视频编码标准MPEG-4增加了适于多种应用的基于视频内容的功能,为了支持这一功能和提高编码效率,MPEG-4将视频序列中的每一帧分解成视频对象面(VOP);另外,由于基于内容的视频检索和视频监控系统均期望用分割出的关键视频对象紧致地表示一个序列,同时由于视频分割技术在模式识别、计算机视觉等领域也得到了广泛的应用,因此,分割视频运动物体并跟踪运动物体的变化变得至关重要.为了对视频中运动物体进行有效的分割,在帧差图象的基础上,采用Canny边缘检测和随机信号的高阶矩检测相结合的方法,来自动分割视频序列的前景区域和背景区域,并在前景区域中应用区域生长法进行颜色分割,以精确提取运动物体的边缘;还利用边缘和颜色特征来对分割出的运动物体建立模板,用于解决非刚体运动中局部暂时停止运动的情况.实验结果表明,此方法可以有效地分割运动物体,并能跟踪运动物体的变化.

Automatic Segmentation of Moving Objects in Video Sequences Using Multifeature

The video coding standard MPEG-4 enables content-based functionality for various applications. To support this functionality and improve coding efficiency, MPEG-4 decomposes each frame of a video sequence into video object planes (VOPs). The technique is also used in content-based video retrieval and video monitoring system to improve intelligence degree; and its applications are extended to pattern recognition, computer vision et al. Thus, segmentation and tracking of video moving objects become a key related research topic. A novel algorithm for automatic segmentation and tracking of moving objects is proposed in this paper. On the base of different image, we utilize a method of combining Canny edge detection and fourth-orders statistic hypothesis testing in improving the moving areas separation results. Then we use region growing with color information in the moving areas to achieve precise boundary of moving objects; and make use of the last segmentation result, extract edge and color information of moving objects, apply edge model matching and color model matching approach to track the changes of moving objects, and detect temporarily still parts. The experimental results for several video sequences demonstrate that our approach is effective in precise segmentation and detect the temporarily still parts of the video moving objects.