为了能够从视频序列图像中准确地提取出运动视频对象,提出了一种基于改进活动轮廓的视频对象自动分割及跟踪方法。该方法首先采用连续帧间差的4次统计量假设检验来确定视频对象的运动区域,并使用形态滤波消除残余噪声和空洞;然后根据3帧序列图像得到的前后运动区域的相与运算来有效地解决运动视频对象前后帧的遮挡问题,以获得视频对象模板,当提取出视频对象模板的边缘轮廓后,再用梯度向量流场作为外力的改进活动轮廓算法来获得视频对象的精确轮廓;最后以此视频对象的轮廓为基础进行运动补偿,以得到下一帧图像的初始曲线,再使用改进的活动轮廓算法对下一帧图像进行分割,即可实现视频对象的跟踪。该方法不仅能够消除差分图像中的显露背景,得到运动视频对象精确的轮廓,并且可进行多目标的分割与跟踪。

In order to extract the moving video object accurately,a novel algorithm for automatic segmentation and tracking moving object is proposed in this paper.Firstly,a fourth-order statistic hypothesis testing in inter-frame difference is used to automatically separate the moving areas from the background in a general video sequence.Then the morphological filter is used to remove the noises and holes.Based on the intersection operation of the two moving areas which obtained from three consecutive frames,the covered/uncovered background may be detected and moving object model can be obtained.Secondly,after determining the initial contour of the moving object,an improved active contour which uses the gradient vector as the external force guides the initial contour moving to the video object contour.Lastly,the motion compensation is made to the video object contour to obtain the initial contour of the next frame and the improved active contour is used again to correct the video object contour of the next frame.Thus,video object tracking can be realized.Our method can not only obtain the accurate contour of the video object but also make the segmentation and tracking for multiple objects.