导　　师： 田金文;谭毅华

学科专业： 081104

授予学位： 博士

作　　者： ;

机构地区： 华中科技大学

摘　　要： 随着计算机科学、电子技术、自动控制以及人工智能的发展与普及，智能视频监控技术已被广泛应用到国民经济的各领域，它在军事、工业、智能人机交互、智能交通和科学研究等方面都具有重要的意义，应用前景广阔。作为智能视频监控的核心关键技术，序列图像目标检测与跟踪在理论和应用上仍存在许多问题和难点尚未解决。很多因素都会影响在序列图像中对运动目标的可靠观测，例如，视觉特征分辨力较弱、背景干扰、目标间相互遮挡，加上实际环境中目标运动的随机性和复杂性/(目标大小、形变、运动速度、光照变化、目标颜色与背景颜色的相似程度等/)，这些都使得算法的设计变得非常困难。因此，研究复杂场景下的目标检测与跟踪具有重要的理论意义和很高的实用价值。论文针对复杂场景中运动目标检测与跟踪中存在的若干问题，对运动目标的准确分割、区域特征提取、目标描述及鲁棒跟踪等关键技术进行了研究与探讨。论文的主要研究内容和成果概括如下： /(1/)对静态背景下运动目标的检测与分割进行了研究。针对复杂背景、光照变化、阴影等影响目标检测的问题，提出了一种有效的自适应混合高斯背景建模算法，各像素点根据其像素值出现的混乱程度采取不同个数的高斯分布描述，重新设计了背景模型的学习与更新方法以及高斯分布生成准则；采用基于联合像素时空信息的分割与形态学重构方法使得前景目标分割的性能得到了有效地提高。同时文中也给出了阴影检测与抑制、光照突变的处理方法。该算法能够快速准确地建立背景模型，准确分割前景目标。 /(2/)针对固定监控场景提出了一种基于色彩分割与目标局部模型匹配的非刚性目标跟踪算法。利用自适应混合高斯背景模型提取前景运动目标，通过 With the development and popularization of computer science, electronic technology,automatic control and artificial intelligence, the intelligent visual surveillance plays veryimportant roles in military, industry, human-computer interaction, intelligent transformand science researching, etc, and it is of a wide development prospect as well. So peoplepay more and more attention to the researches of sequential images motion detection andvisual tracking, which is a key technology of the intelligent visual surveillance and is anactive research topic in the image processing and computer vision. At present, movingtarget detection and tracking is not well-considered, many problems and difficult points intheory research and in applications are still unsolved. However, many issues such as weakdistinguishing image features, background clutter, occlusion, in addition, the targetmovement often behaves very complicated in real environment /(e.g. target size, shapechanging, move speed and path, illumination changing, the similarity of target color andbackground color, and background stability, etc/), can affect the effective observation of thetracked targets in images and make robust tracking algorithms designing a very difficultproblem. Therefore, the researching on moving target detection and tracking undercomplicated background has both important theory significance and application value.Aiming at resolving the difficult problems of the robust visual tracking, we studied severalkey technologies under complicated environment from movement target detection andsegmentation, target area feature acquiring, target describing, and robust tracking. Themain contents and contributions of this dissertation are summarized as follows: /(1/) The moving target detection and segmentation have been studied in stationaryscene. An effective adaptive background updating method based on Gaussian mixturemodel /(GMM/) was presented. The number of mixture components of GMM is estimatedaccording to the frequency of pixel value changes, the pe

关 键 词： 序列图像分析 运动目标检测 目标跟踪 背景建模 核密度估计 粒子滤波 复杂场景

领　　域： [自动化与计算机技术] [自动化与计算机技术]