【作者】 张宗杰；

【导师】 龚声蓉；

【作者基本信息】 苏州大学 ， 计算机应用技术， 2007， 硕士

【摘要】 视频监控中运动目标的检测与跟踪是计算机视觉和图像编码领域的重要研究项目之一,在军事、医学和科研等领域都有广泛的应用。运动目标检测与跟踪算法的设计直接影响跟踪效果的准确性和稳定性。本文主要研究视频监控中运动目标的检测与跟踪的关键问题。在运动目标检测方面,首先对目前流行的帧间差法、背景差法、光流场法进行了实验分析和比较,指出其优缺点及适用范围:对于静态背景的获取、估计与更新进行了实验研究,引入了分块处理的思想,提出了分块背景估计算法,该方法增强了运动目标检测随环境变化的鲁棒性;提出一种基于码书的运动目标检测方法。该方法用矢量量化/聚类技术构建背景模型,利用当前帧和背景帧之间的亮度偏差和色度偏差来检测运动目标。实验表明,该方法具有很好的检测效果。在运动目标跟踪方面,对常用的视频运动目标跟踪方法进行了分析比较,提出一种利用差分法和特征匹配进行目标跟踪的方法。该方法将差分法得到的目标用矩形框框起来,对矩形框内的目标求质心,然后利用质心结合形状特征进行匹配,实验表明,该方法可以较好的适应目标形状有一定变化的情况,简单易行,在情况不复杂的情况下可以较好地跟踪目标。最后,本文对Mean Shift目标跟踪方法从实现原理、匹配准则和搜索算法等几方面对进行了分析。更多还原

【Abstract】 Motion object detection and tracking in serial images is the main research field in Video Surveillance, which has been widely applied in military, medicine and scientific research etc. The accuracy and stability of tracking effect depend on the design of algorithms to a great extent. The key technologies is studied in this paper about how to detect and track moving object in Video Surveillance.On the research of the motion detection, firstly three main algorithms of motion detection and its analyzed advantages and drawbacks are researched. Secondly, background estimation based on block is proposed to solve the problem of Obtaining and estimating and updating background model which can enhance the robust of moving object detection. A background modeling and subtraction method by codebook construction is proposed. The CB algorithm adopts a quantization and clustering technique to construct a background model and subtract the current image from the background model to detect moving objects by color distortion and bright distortion. Evaluation shows that this algorithm is effective.On the research of object tracking, firstly some common used algorithms of are researched. Then a new method of moving object tracking based on the result of image subtraction and object features matching is presented. The obtained object is framed and its center of gravity is calculated. In this together with its shape feather, its moving path can be obtained. Evaluation shows that this algorithm has better adoption in case of a little shape change and is effective in simple cases. At last the Mean Shift Algorithm is analyzed from the aspects of realization principle and match criterion and search algorithm.更多还原