基于改进光流场目标检测跟踪系统的设计

发布时间：2018-07-03 13:14

本文选题：光流法 + 目标检测　；参考：《安徽理工大学》2017年硕士论文

【摘要】：基于运动目标的检测和跟踪是道路交通监管中经常遇到的一个问题,我们通常是利用模式识别的方法来对视频图像中的车辆进行跟踪。车辆等目标的跟踪就是在视频图像的每个图像中确定我们所需要的目标位置,来实现跟踪。随着科学技术的不断发展,目标检测与跟踪技术越来越智能化。其中基于运动目标的检测跟踪算法在对现实的虚拟、过程控制、军事、医疗、道路交通监控等诸多领域都有广阔的应用前景。而跟踪算法的关键在于如何快速、高效地在每一帧图像中实现自动目标定位。基于光流法的算法广泛应用于目标检测和跟踪系统。本篇论文中介绍了几种常用的目标检测与目标跟踪算法,通过将各算法之间的优缺点进行比较,本文提出了改进的目标检测与跟踪算法,最后进行仿真实验,从实验结果发现改进的算法具有一定的准确性。从以下几个方面论述了本文的主要研究内容以及创新点:介绍了三种经典的运动目标检测算法,背景差分法(background difference method)、帧差法(frame difference method)、光流法(optical flow method)以及基于光流法的运动目标检测与跟踪改进算法。简要介绍了三种算法的基本原理,并将各种算法的优缺点进行了对比。BDM检测对其动态背景变化的检测十分敏感;FDM尽管其计算量比其它算法要小,但当用来对运动目标进行检测时会发生"空洞"的现象;OF虽在复杂环境条件下能进行运动目标的检测,却容易收到噪声的干扰。同时通用的光流算法耗时较大不能满足实时性要求,且不能精确检测出运动目标的外形。因此,对传统算法存在的一些缺陷,本文提出了特征光流法(Characteristic optical flow method)和ViBe算法组成新型的光流计算模型对视频图像中运动目标进行准确检测获得图像的稠密纹理流,将运动物体的边缘信息与光流运动的信息进行多特征融合,从计算结果的精度和稳健性两方面进行评估。对于运动目标的跟踪,本文主要采用的是基于扩展性卡尔曼滤波的光流法的跟踪方法。扩展性卡尔曼滤波算法能很好的解决非线性观测下目标的跟踪问题。当运动目标遇到遮挡时,扩展性卡尔曼滤波能很好的对运动目标进行估计与预测。通过Matlab仿真实验结果分析,扩展性卡尔曼滤波技术对运动目标进行跟踪时,其运算范围更广泛,运算速度更快结果更精确。
[Abstract]:Detection and tracking based on moving targets is a problem often encountered in the supervision of road traffic. We usually use pattern recognition method to track the vehicle in the video image. The tracking of the target of the vehicle is to determine the target position we need in each image of the video image to achieve the tracking. With the continuous development of learning technology, the technology of target detection and tracking is becoming more and more intelligent. The detection and tracking algorithm based on moving targets has broad application prospects in many fields, such as virtual reality, process control, military, medical, road traffic monitoring and so on. The key of tracking algorithm is how to quickly and efficiently in each frame. The algorithm based on optical flow method is widely used in target detection and tracking system. In this paper, several common target detection and target tracking algorithms are introduced. By comparing the advantages and disadvantages of each algorithm, an improved target detection and tracking algorithm is proposed in this paper. Finally, simulation experiments are carried out. The results show that the improved algorithm has certain accuracy. The main research contents and innovation points are discussed from the following aspects: three classical algorithms of moving target detection, background difference (background difference method), frame difference (frame difference method), optical flow (optical flow method) and base An improved algorithm for moving target detection and tracking in the light flow method. The basic principles of the three algorithms are briefly introduced, and the advantages and disadvantages of the various algorithms are compared with the.BDM detection, which is sensitive to the detection of dynamic background changes; FDM, although its computation is smaller than other algorithms, will be "empty" when used to detect moving targets. The phenomenon of "hole"; although OF can detect the moving target in complex environment, it is easy to get noise interference. At the same time, the common optical flow algorithm is time-consuming and can not meet the real-time requirements, and can not accurately detect the shape of the moving target. Therefore, the feature optical flow method (Charac) is proposed in this paper. Teristic optical flow method) and ViBe algorithm form a new optical flow calculation model to detect the moving target in the video image accurately and obtain the dense texture flow of the image. It combines the edge information of the moving object with the information of the optical flow motion, and evaluates the accuracy and robustness of the results from two aspects. Following the tracking of moving targets, this paper mainly adopts the tracking method based on the optical flow method based on the extended Calman filter. The extended Calman filtering algorithm can solve the tracking problem of the target under the nonlinear observation. When the moving target meets the occlusion, the extended Calman filter can estimate and predict the moving target very well. Matlab simulation experiment results analysis, the extended Calman filter technology for moving target tracking, its operation range is more extensive, faster operation results more accurate.
【学位授予单位】：安徽理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U495;TP391.41

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