【作者】 杨明；

【导师】 邹北骥；

【作者基本信息】 中南大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 随着计算机技术的迅速发展,基于视频的运动分析已成为计算机视觉领域中的一个研究热点。作为运动物体视觉分析中的核心技术和底层问题,将视频中的运动对象从动态场景中分割出来有着十分重要的意义,它是后续各种高级处理,如运动对象跟踪、物体分类及行为识别、事件检测等高层次的视频处理和应用理解的基础。论文介绍了视频对象分割技术在多媒体标准、视频监控、体育运动分析等领域的应用和研究状况,讨论了其在当今高速发展的信息时代的重要意义和应用价值,综述了国内外视频分割技术的发展现状,从空域帧内分割、全局运动估计、时空联合分割等几方面讨论了视频分割方法。并针对跳水运动视频的特点,对如何从动态场景中提取运动对象的问题开展研究。在此基础上,将视频对象提取方法应用于跳水和体操运动视频。论文针对视频图像的运动估计问题开展研究,提出了一种无回溯搜索的快速块匹配算法。算法基于快速模式匹配KMP算法思想,将图像中的块匹配转化为一个一维的串模式匹配。利用子块的自匹配特性,计算出子块中每一个像素的next值,当模式中第j个像素与主块中的像素“失配”时,从子块的next数组中找到第r个像素来继续进行比较,不需要回溯到开始匹配的位置,由此提高了搜索匹配块的效率。根据跳水运动视频中的运动员着装比较单一、肤色裸露的特点,将视频帧图像用图的形式表示出来可以划分为较少的连通分量,继而提出了一种基于局部图不规则块匹配视频分割方法。该方法首先将图像以图的数据结构表示,采用假设检验的方法来合并相邻区域的图像块。在分割得到第一帧前景区域之后,根据帧间运动的连续性,运用不规则块匹配方法找到当前帧中的前景区域,匹配准则借鉴了聚类分析中变量相关性的相关系数方法,取得了较好的匹配效果。实验结果表明,本文的方法能快速、准确地从跳水、体操运动视频中分割出视频前景运动对象。更多还原

【Abstract】 With the rapid development of computer science and technology, the motion analysis based on video has become a hot topic in the computer vision field. As a key technology in the vision analysis of moving object, extracting motion objects from a dynamic scene plays a significant role for video analysis. It is also the basis of other motion analyses, such as motion object tracing, object classification and activity recognition, event detection and so on.This dissertation introduces some applications and developments of video object segmentation techniques in aspects of multimedia standard, video surveillance and sports analysis. It also summarizes research works of video segmentation from all over the world, and analyzes the video object segmentation methods, such as spatial intra-frame segmentations, global motion estimations and spatio-temporal segmentations. According to the characteristics of diving sport, we do research on how to retrieve the moving objects from a dynamic scene video.A non-backtracking searching algorithm for block matching in video is proposed to address the problem of motion estimation. Based on the idea of the KMP algorithm of character string for fast pattern matching, the algorithm converts the block matching in the video image into the pattern matching of a character string. The next function value of each pixel in the sub-block can be easily gotten by taking advantage of the self-matching trait of the block. And the r-th pixel from the next array of the block can be also found out so that to keep the comparing without backtracks to the start position of matching while the j-th pixel matching is lost.Because the diving athletes always have simple dresses and naked skins, we can get a few connected components by representing the video frames by graph. In view of this assertion, an irregular block matching video segmentation method based on the local graph is proposed. First, images are represented by the graph data-structure, and then the hypothesis test is adopted to merge the adjacent image block. After segmenting the first frame, according to the continuity of the frame motion, it takes advantage of the irregular block matching method to find the foreground area in the current frame and take the correlation coefficient method of variable correlation in the clustering analysis as the matching criteria for reference. Experimental results on diving and gymnastics videos show that the proposed method can segment the video motion objects efficiently.更多还原