基于机器视觉的目标检测与跟踪研究

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基于机器视觉的目标检测与跟踪研究(任务书,开题报告,外文翻译,论文15000字)
摘要
随着计算机技术的飞速发展,目标检测与跟踪技术有了很大的提高,它已经成为智能监控、智能交通等领域的重要构成部分。该技术对于行车安全和行人安全有着非常重要的意义,特别是行人的检测与跟踪。在我们生活的城市里,交通环境是比较复杂的。
本文首先研究了运动目标检测,当背景是静态时,其方法主要是背景减除法、三帧差分法、光流法,然后分析了各自的特点与不足。当背景是动态时,该部分是本文的核心部分之一,提出了改进的灰度投影算法,能够估计运动背景的平移,缩放等参数,并且准确地在动态背景下对运动目标检测其次是人形目标识别,在完成运动目标检测的基础上,通过宽高比,周长,矩形度等图形特征识别运动目标中的人形目标。最后是运动目标跟踪,提出了基于多特征与Kalman滤波融合的Meanshift跟踪算法,能够对多个目标进行快速有效地跟踪,同样是本文核心部分。通过实验可以证明本文所提出的算法无论在静态还是动态的背景下,都能完成运动目标准确实时地检测跟踪。
关键词:检测,跟踪,动态背景,行人监控
Abstract
With the fast advance of computer technology, target detection and tracking technology has been greatly improved. It has become an important part of intelligent monitoring, intelligent transportation and other fields. This technology is very important for traffic safety and pedestrian safety, especially for pedestrian detection and tracking. In the cities where we live, the traffic environment is relatively complex.
Firstly, this paper studies the detection of moving objects. When the background is static, the main methods are background subtraction, three frame difference and optical flow. Then, the characteristics and shortcomings of each method are analyzed. At that time, this part is one of the core parts of this paper. An improved gray projection algorithm is proposed, which can estimate the parameters of moving background such as translation and scaling, and accurately detect moving target in dynamic background, followed by human object recognition. When it is completing moving target detection, the width-height ratio, circumference, rectangularity and other graphical features are adopted. Feature recognition of human-shaped objects in moving targets. Finally, we propose algorithms to track shifting based on motion tracking target for several properties and fusion to wait. The Kalman, which can track multiple targets quickly and efficiently. It is also the core part of this paper. Experiments show that the proposed algorithm can detect and track moving objects accurately and real-time in both static and dynamic environments.
Key words: detection, tracking, dynamic background, pedestrian monitoring
 

基于机器视觉的目标检测与跟踪研究
基于机器视觉的目标检测与跟踪研究


目录
第1章 绪论    1
1.1 课题的研究背景和意义    1
1.2关键技术发展现状    1
1.2.1目标检测技术的发展现状    1
1.2.2目标跟踪技术的发展现状    2
1.2.3行人检测与跟踪技术的发展现状    3
1.3论文的章节安排    4
1.4本章小结    4
第2章 目标检测研究    6
2.1基本检测方法    6
2.1.1背景减除法    6
2.1.2帧差法    6
2.1.3光流法    6
2.2动态背景下运动目标检测    7
2.2.1背景运动补偿    7
2.2.2改进的三帧差分法    8
2.3实验结果分析    9
2.4本章小结    10
第3章人形目标识别    11
3.1人形目标特征选择    11
3.2人形目标特征提取    12
3.2.1确定目标边界    12
3.2.2提取目标的周长    12
3.2.3计算目标的长宽比    13
3.2.4计算目标的矩形度    13
3.3运动行人识别算法    13
3.4本章小结    14
第4章目标跟踪研究    15
4.1基于多特征与Kalman滤波结合的Meanshift算法    15
4.1.1Meanshift算法的基本原理    15
4.1.2多特征提取    15
4.1.3Meanshift跟踪算法    17
4.1.4 Kalman滤波    18
4.1.5多特征与Kalman滤波结合的Meanshift算法    19
4.2实验结果分析    21
4.3本章小结    22
第5章总结与展望    23
5.1总结    23
5.2展望    23
参考文献    24
致谢    25