智能交通系统中运动目标的自动分析技术研究
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摘要
智能交通系统(ITS)是一种融合多学科知识，集信息采集、处理、存储与发布为一体，实时服务于交通运输与管理，极具应用前景的智能系统。随着视频监控系统的普及和视频图像处理技术的进步，以视频图像处理为基础的ITS应用研究越来越受到重视。为使ITS更加智能实用，能处理诸如事故信息判断、车辆分类、交通流参数检测、运动目标跟踪等各种问题，交通视频图像处理的算法研究具有重要的现实意义和理论价值。
本文以交通视频中运动目标为研究对象，重点开展视频和图像处理算法研究。研究内容包含运动目标的检测、提取、识别、跟踪以及交通流参数的计算与分析等，为ITS的智能化提供技术支持。主要内容包括以下几个方面：
结合运动检测法和图像分割法，对运动目标进行提取。针对交通视频复杂多变的特征，在讨论正交Gaussian Hermite矩(Orthogonal Gaussian Hermite Moments，OGHM)运动目标检测的基础上，通过建立Markov模型，对交通视频中的单帧图像进行分割；为了优化算法、降低时间复杂度，引入Graph Cuts法进行加速；为提高运动目标提取的智能化水平，提出了种子像素扩展法，实现了运动目标的自动提取。
以OGHM法、形态学、距离图像求解和阈值分割等方法为基础，结合Matting算法中的Graph Cuts和全局优化算法，提出了一种基于自动生成Scribbles的运动目标自动提取方法。由于对前景和背景部分都分别给予了描绘，运动目标提取的效果有明显改进。通过实验，比较了GC算法与全局优化算法的区别，分析了自动生成Scribbles时参数不同对结果的影响。实验结果表明，该运动目标提取算法能有效降低复杂背景的干扰，且比前一个算法的效果要更好。
为提高对目标多样性和复杂性的适应能力，本文提出了基于自适应综合特征的目标识别与匹配算法。首先选择颜色、边缘和矩作为单一特征来描述目标，提取目标的HSI颜色直方图向量、边缘直方图向量和HU不变矩向量，再以图像距离为相似性度量，随机区域加权估计为搜索策略，构建目标匹配模型。其中，每个单一特征的权值可根据单一特征的匹配结果动态调整。实验证明，综合特征匹配的效果要好于单一特征匹配的效果。
以粒子滤波理论为基础，配合本文提出的目标识别与匹配算法，提出了以二阶常数模型为运动模型、自适应综合特征模型为观测模型的运动目标跟踪方法。其中，观测模型中的各单一特征似然函数的方差和权重均能够根据前一帧的跟踪结果实时更新，实现观测模型的自适应性，从而提高了运动目标跟踪的准确性。
搭建了交通流的测试平台，利用以上算法对交通流参数进行了检测，实现了理论到实践的转化，为改善和提高交通视频监控能力提供了基础。
Intelligent Transportation System (ITS) is a promising intelligent system providingreal-time service for traffic management. With the combination of multiple fields, the ITSconsists of the acquisition, processing, storage and distribution of information. With thedevelopment of video monitoring systems and the advances in video image processingtechniques, application research based on video image processing of ITS has beenwidespread and deepgoing. It is of great practical value and theoretic importance to performan better research on video image processing algorithms of traffic video in order to make theITS more intelligent and functional in handling problems such as accident judgment, vehicleclassification, traffic flow parameters detection and moving target tracking.
With the moving targets in traffic video as the main object, this thesis focuses on theresearch of video and image processing algorithms. The content is comprised of detection,extraction, identification and tracking of moving targets, as well as calculation andidentification of the traffic flow parameters, providing support for the intelligentizing ofITS. The primary contents are as follows:
The moving targets were extracted with motion detection method and imagesegmentation method combined. First moving targets detection based on the OrthogonalGaussian Hermite Moments were introduced for the complexity of traffic video. Then imagesegmentation of a single frame from the traffic video was performed with Markov model. Inorder to reduce the time complexity of the algorithm, Graph Cuts method was adopted. Seedpixel extension method was proposed and mattes of the moving targets were generated basedon the two aforementioned methods, thus realizing automatic extraction.
An automatic moving target extraction method based on auto-generated Scribbles wasproposed by combining Orthogonal Gaussian Hermite Moments, morphology processing,distance transformation and threshold segmentation. Performance of method is superior tothe method proposed in the chapter above for it depicted both the foreground and the background. Through experiments, comparisons were made between Graph Cuts and Close-formSolution algorithms, and the influence of the parameters during Sciribble auto-generationwas analyzed, providing conditions for further processing.
The target recognition and matching based on adaptive multiple features algorithm wereproposed. Color, edge and moment were selected as single feature in describing the targetsfirst, and HSI Color Histogram, Edge Orientation Histogram and HU invariant moments ofmoving targets were extracted. Then image distance was selected as the similaritymeasurement and random region weighted estimate was proposed as the exploration policy,where the weight of each single feature could be altered according to the result of singlefeature matching.
Following the theory of partical filtering theory, according to the methods in last chapter,with a second order constant model as the state model and an adaptive fusion multi-featuresmodel as the observation model, a moving target tracking method was proposed. Thevariance and weight of the likelihood functions of each single feature in observation modelcould be real-time updated according to the tracking results of previous frame, thus resultingin an adaptive observation model and increasing the accuracy of moving targets tracking.
A traffic flow test platform was constructed, and the parameters of the traffic flow weredetected, thus realizing the transition from theory to practice and providing basis forimproving the capability of traffic video monitoring.
本文以交通视频中运动目标为研究对象，重点开展视频和图像处理算法研究。研究内容包含运动目标的检测、提取、识别、跟踪以及交通流参数的计算与分析等，为ITS的智能化提供技术支持。主要内容包括以下几个方面：
结合运动检测法和图像分割法，对运动目标进行提取。针对交通视频复杂多变的特征，在讨论正交Gaussian Hermite矩(Orthogonal Gaussian Hermite Moments，OGHM)运动目标检测的基础上，通过建立Markov模型，对交通视频中的单帧图像进行分割；为了优化算法、降低时间复杂度，引入Graph Cuts法进行加速；为提高运动目标提取的智能化水平，提出了种子像素扩展法，实现了运动目标的自动提取。
以OGHM法、形态学、距离图像求解和阈值分割等方法为基础，结合Matting算法中的Graph Cuts和全局优化算法，提出了一种基于自动生成Scribbles的运动目标自动提取方法。由于对前景和背景部分都分别给予了描绘，运动目标提取的效果有明显改进。通过实验，比较了GC算法与全局优化算法的区别，分析了自动生成Scribbles时参数不同对结果的影响。实验结果表明，该运动目标提取算法能有效降低复杂背景的干扰，且比前一个算法的效果要更好。
为提高对目标多样性和复杂性的适应能力，本文提出了基于自适应综合特征的目标识别与匹配算法。首先选择颜色、边缘和矩作为单一特征来描述目标，提取目标的HSI颜色直方图向量、边缘直方图向量和HU不变矩向量，再以图像距离为相似性度量，随机区域加权估计为搜索策略，构建目标匹配模型。其中，每个单一特征的权值可根据单一特征的匹配结果动态调整。实验证明，综合特征匹配的效果要好于单一特征匹配的效果。
以粒子滤波理论为基础，配合本文提出的目标识别与匹配算法，提出了以二阶常数模型为运动模型、自适应综合特征模型为观测模型的运动目标跟踪方法。其中，观测模型中的各单一特征似然函数的方差和权重均能够根据前一帧的跟踪结果实时更新，实现观测模型的自适应性，从而提高了运动目标跟踪的准确性。
搭建了交通流的测试平台，利用以上算法对交通流参数进行了检测，实现了理论到实践的转化，为改善和提高交通视频监控能力提供了基础。
Intelligent Transportation System (ITS) is a promising intelligent system providingreal-time service for traffic management. With the combination of multiple fields, the ITSconsists of the acquisition, processing, storage and distribution of information. With thedevelopment of video monitoring systems and the advances in video image processingtechniques, application research based on video image processing of ITS has beenwidespread and deepgoing. It is of great practical value and theoretic importance to performan better research on video image processing algorithms of traffic video in order to make theITS more intelligent and functional in handling problems such as accident judgment, vehicleclassification, traffic flow parameters detection and moving target tracking.
With the moving targets in traffic video as the main object, this thesis focuses on theresearch of video and image processing algorithms. The content is comprised of detection,extraction, identification and tracking of moving targets, as well as calculation andidentification of the traffic flow parameters, providing support for the intelligentizing ofITS. The primary contents are as follows:
The moving targets were extracted with motion detection method and imagesegmentation method combined. First moving targets detection based on the OrthogonalGaussian Hermite Moments were introduced for the complexity of traffic video. Then imagesegmentation of a single frame from the traffic video was performed with Markov model. Inorder to reduce the time complexity of the algorithm, Graph Cuts method was adopted. Seedpixel extension method was proposed and mattes of the moving targets were generated basedon the two aforementioned methods, thus realizing automatic extraction.
An automatic moving target extraction method based on auto-generated Scribbles wasproposed by combining Orthogonal Gaussian Hermite Moments, morphology processing,distance transformation and threshold segmentation. Performance of method is superior tothe method proposed in the chapter above for it depicted both the foreground and the background. Through experiments, comparisons were made between Graph Cuts and Close-formSolution algorithms, and the influence of the parameters during Sciribble auto-generationwas analyzed, providing conditions for further processing.
The target recognition and matching based on adaptive multiple features algorithm wereproposed. Color, edge and moment were selected as single feature in describing the targetsfirst, and HSI Color Histogram, Edge Orientation Histogram and HU invariant moments ofmoving targets were extracted. Then image distance was selected as the similaritymeasurement and random region weighted estimate was proposed as the exploration policy,where the weight of each single feature could be altered according to the result of singlefeature matching.
Following the theory of partical filtering theory, according to the methods in last chapter,with a second order constant model as the state model and an adaptive fusion multi-featuresmodel as the observation model, a moving target tracking method was proposed. Thevariance and weight of the likelihood functions of each single feature in observation modelcould be real-time updated according to the tracking results of previous frame, thus resultingin an adaptive observation model and increasing the accuracy of moving targets tracking.
A traffic flow test platform was constructed, and the parameters of the traffic flow weredetected, thus realizing the transition from theory to practice and providing basis forimproving the capability of traffic video monitoring.
引文
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