基于图像序列的运动目标检测与跟踪研究

发布时间：2019-03-02 16:05

【摘要】：运动目标检测与跟踪是计算视觉领域内一个重要研究方向,它融合了图像处理、模式识别、深度学习、人工智能等相关领域的知识,广泛应用在视频监控、智能机器人、工业检测等领域,并且有着极大的发展前景。运动目标检测与跟踪是指获取的图像序列中的运动目标,返回目标实时位置并且对运动目标进行跟踪。根据摄像头与运动目标是否相对运动,可分为静态背景下的运动目标检测与跟踪和动态背景下的运动目标检测与跟踪。本文主要研究静态背景下的运动目标检测与跟踪。本文首先介绍了数字图像处理方面的基础知识,如常见的颜色空间模型、数学形态学处理方法;还分析了噪声的特性和一些常见噪声来源,并且给了中值滤波、高斯滤波、小波变换等去噪方法。在运动目标检测方面,介绍了背景差分法、帧间差分法和光流法三种常见的运动目标检测方法,并且给出了它们适用的场景以及主要优缺点。针对背景差分法中背景模型不能实时反映背景变化的缺点,本文提出了一种将混合高斯背景模型与背景差分法结合的运动目标检测方法,用多高斯背景模型描述背景的变化,随着图像序列的输入动态的更新背景模型,然后与当前帧图像作差从而检测出目标。在运动目标跟踪方面,首先介绍了几种常用的运动目标跟踪方法,包括基于轮廓的跟踪方法、基于模型的跟踪方法、基于特征点的跟踪方法和基于区域的跟踪方法。主要研究了MeanShift算法和Camshift算法,并针对Camshift算法的不足,本文提出了一种将Kalman滤波与Camshift算法线性拟合的跟踪方法,在目标跟踪过程中加入了Kalman滤波对目标位置的预测,在物体被背景中其它物体遮挡时还能准确的跟踪到目标物体。基于前述方法,本文在Visual Studio 2010集成开发平台使用OPENCV视觉库和C++编程语言开发了基于摄像头的运动目标跟踪系统,该系统具有目标检测、目标跟踪。保存跟踪视频等功能。实验结果表明,本系统能较好的实现复杂背景下的运动目标跟踪。
[Abstract]:Moving object detection and tracking is an important research direction in the field of computational vision. It combines the knowledge of image processing, pattern recognition, deep learning, artificial intelligence and other related fields. It is widely used in video surveillance, intelligent robot, and so on. Industrial detection and other fields, and has a great prospect for development. Moving object detection and tracking refers to the moving object in the image sequence, which returns the real-time position of the target and tracks the moving object. According to the relative movement of camera and moving object, it can be divided into static background detection and tracking of moving object and dynamic background of moving object detection and tracking. This paper mainly studies moving target detection and tracking under static background. This paper first introduces the basic knowledge of digital image processing, such as the common color space model, mathematical morphology processing methods; The characteristics of noise and some common noise sources are also analyzed, and de-noising methods such as median filtering, Gao Si filtering and wavelet transform are given. In the aspect of moving target detection, the background difference method, the inter-frame difference method and the optical flow method are introduced, and their applicable scenes and main advantages and disadvantages are given. In view of the disadvantage that the background model can not reflect the background change in real time in the background difference method, this paper proposes a moving target detection method which combines the mixed Gao Si background model and the background difference method, and describes the background change with the multi-Gao Si background model. The background model is updated dynamically with the input of the image sequence, and then the target is detected by the difference from the current frame image. In the aspect of moving target tracking, several commonly used tracking methods are introduced firstly, including contour-based tracking method, model-based tracking method, feature-point-based tracking method and region-based tracking method. This paper mainly studies the MeanShift algorithm and the Camshift algorithm, and aiming at the deficiency of the Camshift algorithm, this paper proposes a linear fitting method between the Kalman filter and the Camshift algorithm, and adds the Kalman filter to the target position prediction in the target tracking process. When the object is occluded by other objects in the background, it can also accurately track the target object. Based on the above-mentioned methods, a camera-based moving target tracking system is developed in Visual Studio 2010 integrated development platform using OPENCV visual library and C programming language. The system has the functions of target detection and target tracking. Save tracking video and other functions. The experimental results show that the system can effectively track moving objects in complex background.
【学位授予单位】：江苏科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41

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