摘要
随着计算机硬件和软件的发展,计算机视觉技术受到了人们越来越多的关注。尤其在军事、航空航天、计算机辅助设计、智能机器人等领域,计算机视觉技术得到了广泛的应用。在计算机视觉技术中,基于视频序列的视觉技术是最为重要的,因此也成为人们研究的重点和难点。视频序列中运动目标的检测与跟踪,又是基于视频序列的计算机视觉技术的基础和关键,所以对它们的研究就显得格外的重要。
本文在阅读相关文献的基础上,对运动目标检测方法的发展及研究现状进行了总结。然后介绍了马尔可夫随机场(MRF)和期望最大化(EM)算法的相关概念和基本原理。
本文实现了一种基于信念传播算法的运动目标检测方法。该方法采用基于帧差图像的运动目标检测方法,首先根据差分图像采用期望最大化算法将图像中每个像素分为目标与背景两类。因其结果中会存在一些噪声,需要一种平滑算法消除噪声。本文选择平滑效果较好的信念传播算法实现图像平滑,该算法结合马尔可夫随机场模型与图模式理论,根据每个像素节点的信念计算其状态值,通过消息传播将每个节点的信念传播到其邻域,从而实现了数据的平滑。本文在定义信念传播算法的相容函数时结合了空间梯度信息——梯度算子,用梯度算子约束数据平滑,使平滑效果更为准确。本文采用一种的基于方向信息的边缘提取算子,该算子提取的边缘比普通边缘提取算子的结果更完整。
由于两帧差分图像信息本身不完整的特点,经平滑后的结果仍有可能覆盖不了完整目标。通过计算目标区域最近的闭合边缘的方法估计出准确的目标。
文中最后介绍了算法的仿真实现并给出了实验结果。实验证明,该算法能较好地分割出运动目标。
With the development of hardware and software of computer, computer vision technology is attracted more and more attention by people. Especially in these fields, which include military affairs、aviation spaceflight、CAD、smart android and so on, computer vision technology is used widely. The vision technology which is based on video frequency image sequences is the most important computer vision technology, so it has became hot points and difficult points in investigate works of people. Motion objects detection and tracking which is based on video frequency ,is also the basic and key of computer vision technology.
First, on the basis of studying related literatures, methods and development of motion object detection are introduced. Second, the related concept of the basic principles about Markov Random Field (MRF) and Expectation Maximum(EM)are introduced and methods are studied in detail.
In this paper, a motion object detection method based on belief propagation is proposed, which is based on the time-differenced image of the moving object detection method. In the method, pixels in the image will be divided into two types of background and target, according to the difference image. But there are some noises in the result and a smooth algorithm to remove the noise is needed to make the result more precisely. In the time-differenced image, motion pixels are mainly focused on the edge of the larger gradient, and the goal is often more empty, so a belief propagation algorithm with a larger effort to disseminate the trust is chosen to smooth, which is combined with a gradient of space-gradient operator. Gradient operator is used to constrain smooth and more accurate results can be got. Results from Expectation Maximum(EM) algorithm for classification are smoothed in order to achieve the target partition movement.
Finally, the algorithm describes the realization of simulation, experiment is performed and results are given. It proves that the algorithm has a better performance for motion objects detection.
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