人工智能-图像处理-视频图像处理中的关键技术研究.pdf资源-CSDN文库

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摘要

第 II 页

更好的保存图像的边缘，扩大尺度空间极值点检测的范围，减少关键点的个数，加快关

键点检测的速度，使用 Sobel 算子计算高斯模型的图像的梯度和方向，增强 SIFT 描述

子的区分性，K-means 聚类初始匹配的角度和尺度，去除误匹配，提高匹配的精度，然

后采用 Delaunay 三角剖分的方法，利用其剖分的唯一性，对初始匹配点集合构建三角

网，找到匹配的三角形，从而实现鲁棒的匹配。

（4）针对复杂背景，光照变化，部分遮挡情况下的运动目标跟踪问题，提出基于交

互多模模型粒子滤波的多特征自适应融合的目标跟踪算法。该算法采用多模粒子滤波，

多模是指多个特征，用了去除背景显著特征干扰的校正背景加权直方图，对噪声不敏感

而且具有良好分类精度的完备局部模式，描述局部形状和表观的方向梯度直方图，这三

种特征来建模目标的特征表示，利用交互多模模型能够自适应调整模型的概率，在粒子

滤波框架内进行自适应融合跟踪。多特征融合提高了跟踪算法对于复杂背景，部分遮挡

的鲁棒性。

（5）针对运动目标识别，以人体运动目标为模板，提出基于时空关键点描述子和

运动模板的目标识别算法。首先对人体目标区域采用改进 VIBE 算法进行运动目标检测，

得到的感兴趣区域(ROI)序列，计算相应的运动能量图像和运动历史图像。时空关键点

的检测采用 3DHarris 检测器，关键点描述子采用 3DSIFT，全局特征用 Hu 矩不变量来

表示，进行局部特征和全局特征融合。训练分类器的模型采用支持向量机，码书构建采

用词袋模型。

（6）针对视频图像处理中的运动目标检测、目标匹配、目标跟踪和目标识别等关键

技术存在的不同问题，基于一体化的思想，分析视频图像分析中的不同模块之间的相互

影响，使各个模块之间的能够相互联系和作用，从而优化和提高系统的整体性能，为相

关的部门提供决策和分析的依据。

关键词：运动目标检测，均值漂移聚类，非下采样轮廓波变换，图像融合，粒子滤波，

随机抽样一致性，时空关键点，支持向量机

万方数据

ABSTRACT

第 III 页

ABSTRACT

Image sequence based moving object detection, matching, tracking and recognition is an

emerging field of computer vision of forefront subject and receiving much attention, which

combines computer science, machine vision, image engineering, pattern recognition, artificial

intelligence and other advanced technology, widely used in various aspects of

human-computer interaction, intelligent monitoring, machine vision navigation, industrial

robotics. Video of intelligent information integrating analysis and understanding is a research

hot in the field of machine vision. It is including detection of region of interest, target

matching, target tracking, target identification, image reconstruction, etc. Although there are

many researchers who conduct a lot of related research work, and many simulation

experimental results are made. But these techniques are far from the practical requirement.

Experiments were devised to test and verify the feasibility and effectiveness of the algorithm,

and the specific research contents are listed as follows:

(1) The optimization of image fusion is researched. Based on the properties of

nonsubsampled contourlet transform (NSCT), shift invariance, multiscale and multidirectional

expansion, the fusion parameters of the multiscale decompostion scheme is optimized. In

order to meet the requirement of feedback optimization, a new image fusion quality metric of

image quality index normalized edge association (SSIM-NEA) is built. A polynomial model is

adopted to establish the relationship between the SSIM_NEA metric and several

decomposition levels, guide the fusion process, improved the quality of the fusion image, lay

a foundation for the subsequent moving object detection.

(2) Extracting foreground moving objects from video sequences is an important task and

also a hot topic in computer vision and image processing. Segmentation results can be used in

many object-based video applications such as object-based video coding, content-based video

retrieval, intelligent video surveillance and video-based human–computer interaction. In this

paper, we present a novel moving object detection method based on improved VIBE and

graph cut method from monocular video sequences. Firstly, perform moving object detection

for the current frame based on improved VIBE method to extract the background and

万方数据

ABSTRACT

第 IV 页

foreground information; then obtain the clusters of foreground and background respectively

using mean shift clustering on the background and foreground information; Third, initialize

the S/T Network with corresponding image pixels as nodes (except S/T node); calculate the

data and smoothness term of graph; finally, use max flow/minimum cut to segmentation S/T

network to extract the motion objects. Experimental results on indoor and outdoor videos

demonstrate the efficiency of our proposed method.

(3) Image matching is an important question in computer vision, however, due to the

large viewpoint and similar regions, there exist false matches. A robust matching

method-DelTri is proposed. First, improved the traditional scale invariant feature transform

from four aspects.1) Preprocessing the input image by bilateral filter to preserve the edge;2)

Enlarging search ranged of extrema of keypoints detection to diminish the number of

keypoints;3) Calculate the magnitude and orientation of gauss smoothed image by sobel

operator to smooth the noised ;4) K-means cluster filter uncorrected matches. Based on the

initial matching of improved Scale Invariant Feature Transform, the matched keypoints are

respectively triangulated to create the triangulation net, which can express the overlapped

physical structure of the objects. The matched triangles can lead to the final matches.

(4) Visual tracking could be formulated as a state estimation problem of target

representation based on observations in image sequences. To investigate the integration of

rough models from multiple cues and to explore computationally efficient algorithms, this

paper formulates the problem of multiple cue integration and tracking in particle filter

framework based on interactive multiple model (IMM). IMM can estimate the state of a

dynamic system with several behavioral modes that switch from one to another using mode

likelihoods and mode transition probabilities. For the problem of visual tracking, the models

of IMM are three observation model: Corrected Background-Weighted Histogram (CBWH),

Completed Local Tenary Patterns (CLTP) and Histogram Of Orientation Gradients(HOG).The

models probabilities corresponding to the weight of multiple cue. IMM then dynamically

adjusts the weights of different feature. Compared with state of the art methods, Experimental

results demonstrate that this algorithm can track the object accurately in conditions of rotation,

abrupt shifts, as well as clutter and partial occlusions occurring to the tracking object with

good robustness.

(5)For the moving object recognition, taking the human action as an example, integrating

万方数据

摘要 .....................................................................................................................................................................I

ABSTRACT.........................................................................................................................................................III

第一章绪论........................................................................................................................................................... 1

1.1 概述.............................................................................................................................................................. 1

1.2 课题研究现状.............................................................................................................................................. 2

1.2.1 多传感器图像融合 .............................................................................................................................. 3

1.2.2 运动目标检测 ...................................................................................................................................... 5

1.2.3 目标匹配 .............................................................................................................................................. 7

1.2.4 目标跟踪 .............................................................................................................................................. 8

1.2.5 目标识别 ............................................................................................................................................ 10

1.2.6 视频图像的智能信息一体化分析..................................................................................................... 13

1.3 存在的主要问题........................................................................................................................................ 14

1.4 本文主要工作............................................................................................................................................ 15

1.5 本文内容安排............................................................................................................................................ 18

第二章相关理论与基础知识............................................................................................................................. 20

2.1 概述............................................................................................................................................................ 20

2.2 非下采样 CONTOURLET 变换 ................................................................................................................... 20

2.2.1 NSCT 的结构 ..................................................................................................................................... 20

2.2.2 多尺度分解 ........................................................................................................................................ 21

2.2.3 多方向分解 ........................................................................................................................................ 23

2.2.4 非下采样滤波器组设计 .................................................................................................................... 25

2.2.5 非下采样滤波器组设计图像的非下采样轮廓波变换..................................................................... 26

2.3 运动目标检测 VIBE 算法......................................................................................................................... 27

2.3.1 像素点背景建模 ................................................................................................................................28

2.3.2 模型初始化 ........................................................................................................................................ 28

2.3.3 像素分类 ............................................................................................................................................ 29

2.3.4 模型更新 ............................................................................................................................................ 29

2.4 本章小结.................................................................................................................................................... 29

第三章优化图像融合和运动目标检测............................................................................................................. 30

3.1 概述............................................................................................................................................................ 30

3.2 优化图像融合............................................................................................................................................ 30

万方数据

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