新的视音频编码标准MPEG－4增加了支持基于内容的功能，它把视频序列分割成语义意义上的视频对象（VO）视频对象在某一瞬时的：“快照”称为视频对象平面（VOP），且一系列VOP表示一个运动对象，VOP分割相当困难，这主要是因为物理对象通常不以亮度，彩色或光流等低级特征来表达，所以经典的分割方法无法获得有意义的分割结果，为了对这种视频运动图象进行有效的提取，提出了一种基于修改的豪氏道夫对象踊跃器的自动VOP分割方法，首先提取出初始模型，然后用跟踪器在序列中继帧中跟踪此对象，再对模型逐帧修改，以适应对象在后继帧中形状的旋转和变化，最后根据一系列二值模型来提取出视频对象，此外，为了提高分割效果帮减少复杂性，还使用了静 背景滤除技术来滤除静态背景，实验结果表明，该算法是有效的。

The new video&audio coding standard MPEG-4 is enabling content-based functionalities. It takes advantage of a decomposition of sequences into semantically meaningful video object (VO). A snapshot of a VO is named video object plane(VOP) at a given time and a series of VOPs represent one moving object. This is a very challenging task, because physical objects are normally not homogeneous with respect to low-level features such as color, luminance, or optical flow. Hence, conventional segmentation algorithms will fail to obtain meaningful partitions. In this paper, a new automatic VO segmentation algorithm based on modified Hausdorff object tracker is presented. A binary model for moving object is automatically derived and tracked in subsequent frames using the modified Hausdorff distance. First initial model is extracted and tracked based on the proposed object tracker against subsequent frames in the sequence. Then the model is updated every frame to accommodate for rotation and changes in shape. The video object is extracted by a series of binary models in the end. Furthermore, to improve the quality of segmentation and to reduce the computational complexity , stationary background is filtered by a novel technique. Experimental results demonstrate the performance of our proposed algorithm.