目标跟踪算法的研究

目录

摘要 (1)

ABSTRACT (2)

第一章绪论 (4)

1.1课题研究背景和意义 (4)

1.2国外研究现状 (5)

1.3本文的具体结构安排 (7)

第二章运动目标检测 (8)

2.1检测算法及概述 (8)

2.1.1连续帧间差分法 (9)

2.1.2背景去除法 (11)

2.1.3光流法 (13)

第三章运动目标跟踪方法 (16)

3.1引言 (16)

3.2运动目标跟踪方法 (16)

3.2.1基于特征匹配的跟踪方法 (16)

3.2.2基于区域匹配的跟踪方法 (17)

3.2.3基于模型匹配的跟踪方法 (18)

3.3运动目标搜索算法 (18)

3.3.1绝对平衡搜索法 (18)

3.4绝对平衡搜索法实验结果 (19)

3.4.1归一化互相关搜索法 (21)

3.5归一化互相关搜索法实验结果及分析 (22)

第四章模板更新与轨迹预测 (26)

4.1模板更新简述及策略 (26)

4.2轨迹预测 (28)

4.2.1线性预测 (29)

4.2.2平方预测器 (30)

4.3实验结果及分析： (31)

致 (36)

参考文献 (37)

毕业设计小结 (38)

摘要

图像序列目标跟踪是计算机视觉中的经典问题，它是指在一组图像序列中，根据所需目标模型，实时确定图像中目标所在位置的过程。它最初吸引了军方的关注，逐渐被应用于电视制导炸弹、火控系统等军用备中。序列图像运动目标跟踪是通过对传感器拍摄到的图像序列进行分析，计算出目标在每帧图像上的位置。它是计算机视觉系统的核心，是一项融合了图像处理、模式识别、人工只能和自动控制等领域先进成果的高技术课题，在航天、监控、生物医学和机器人技术等多种领域都有广泛应用。因此，非常有必要研究运动目标的跟踪。

本论文就图像的单目标跟踪问题，本文重点研究了帧间差分法和背景去除法等目标检测方法，研究了模板相关匹配跟踪算法主要是：最小均方误差函数(MES)，最小平均绝对差值函数(MAD)和最大匹配像素统计(MPC)的跟踪算法。在跟踪过程中，由于跟踪设备与目标的相对运动, 视野中的目标可能出现大小、形状、姿态等变化, 加上外界环境中的各种干扰, 所要跟踪的目标和目标所在的场景都发生了变化, 有可能丢失跟踪目标。为了保证跟踪的稳定性和正确性, 需要对模板图像进行自适应更新。由于目标运动有一定得规律，可以采取轨迹预测以提高跟踪精度，本文采用了线性预测法。

对比分析了相关匹配算法的跟踪精度和跟踪速度；对比不采用模板更新和模板跟新的跟踪进度和差别，实验表明，跟踪算法加上轨迹预测及模板跟新在很大程度上提高了跟踪帧数，提高了跟踪精度，具有一定的抗噪声性能。

关键词：目标跟踪，目标检测，轨迹预测，模板更新

ABSTRACT

Target tracking, image sequence is a classic computer vision problems, it is defined as a set of image sequences, in accordance with requirements of the target model, real-time images to determine the location of the target process. It initially attracted the concern of the military has gradually been applied to television-guided bombs, fire control systems for military preparation. Moving target tracking sensor is taken through the image sequence analysis, to calculate the target image in each frame position. It is the core of computer vision system is a combination of image processing, pattern recognition, artificial only and the results of automatic control in areas such as advanced high-tech issues in the aerospace, control, biomedical and robotics fields, etc. There are widely used. Thus, it is necessary to study the tracking of moving targets. In this paper, the image of the single-target tracking problem, research the target detection method is mainly based on inter-frame difference and background removal method to detect the target in preparation for target tracking. Template matching tracking algorithm is: the smallest mean square error function (MES), the smallest mean absolute difference function (MAD) and the maximum matching pixel statistics (MPC) of the tracking algorithm. In the tracking process, due to the relative camera movement with the goal, the goal of vision may occur in size, shape, gesture, such as changes in the external environment combined with the various kinds of interference, as well as over time, to track where the goals and objectives scene changes have taken place, it is possible to track the target is lost. In order to ensure the stability and tracking accuracy, the need for adaptive template image update. Since the goal of