移动目标视频跟踪关键技术的研究

【作者】 姚辉；

【导师】 顾小丰；

【作者基本信息】 电子科技大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 移动目标视频跟踪是当前信息领域的前沿和热点方向,融合了计算机科学、自动控制、机器视觉、图像处理、模式识别、数学等多学科的先进技术。本文以智能视频监控作为主要线索,研究静止背静下运动目标的检测、跟踪等视频跟踪中的关键技术。计算机智能视频监控是在不需要人为干预情况下,利用计算机视觉和视频分析的方法对摄像机拍录的图像序列进行自动分析,实现对动态场景中目标的定位、识别和跟踪,并在此基础上分析和判断目标的行为,得出对图像内容含义的理解以及对客观场景的解释,从而指导和规划行动。这种智能视频监控己在军事和工业上得到一些成功运用,但智能视频监控在理论和运用上都还存在很多难题。当前国内外很多学者投身该领域进行研究和探索,并取得了大量成果,本文是在这些成果的基础上进行的。首先,本文系统地研究和总结了国内外运动目标检测的方法,分析了各方法的利弊、实用场合,在此基础上,重点研究了混合高斯模型,并将极大似然原理引入混合高斯模型,该方法较以往的基于经验值的混合高斯相比,有严密的数学理论作支撑,并且在此方法下的检测效果也能满足固定场合的要求。其次,本文对图像增强、图像去噪和图像分割也作了较系统的总结,并给出了部分实验结果;在此基础上总结出一种图像点、线增强的一般方法。最后,在跟踪方面使用经典的运动分析理论—卡尔曼滤波,对运动目标的下帧所在区域作估计,进而缩小搜索空间,提高检测和跟踪速度。更多还原

【Abstract】 Video tracking of moving object is a hot and latest direction in today’s information technology field. It refers to lots of subjects, including computer science, automation control, computer vision, image processing, pattern recognition and math. The thesis put the intelligent surveillance system as the thread, focused on moving object detection and moving object tracking in stable background.The intelligent surveillance system can automatically analyze the sequence of images by the methods of computer vision and video analysis. The system can position, recognize and track objects in a moving environment. Furthermore, it can also analyze and judge the movement of objects. The aims of this system are to understand the meanings of video stream and to explain the scenes comprehensibly, hence to guide actions and make decisions. Some of the intelligent surveillance systems have successfully been applied to army and industry, but the project still faces lots of challenges. A large number of researchers have been studying the subject and achieved many progresses. This paper was based on these achievements.Firstly, the different kinds of detection methods with their advantages and disadvantages were concluded. We mainly researched the Gauss Mixture Model, and then put the maximizing likelihood idea into the Gauss Mixture Model. The new method was more rigorous, and the detection results can meet the requirements.Secondly, the methods about image enhancement, image filter and image segmentation were concluded, and then some experiment results were given. We extended the image enhancement method, which can used to enhance some dots and threads.Finally, we used the Kalman filter to forecast the position of the moving object in the next frame. The method can reduce the search space in order to improve the efficiency.更多还原