基于特征描述子的视频对象分级分割和跟踪算法

不同的视频应用对视频对象的分割和跟踪的速度和精确度具有不同的要求。提出了一种视频对象的分级分割和跟踪框架,基于视频对象特征描述子算法可以实时地分割和跟踪视频对象,基于区域特征描述子算法对分割的视频对象进一步细化,提高空域准确性。该框架可以满足各种视频应用。     

采用统计推断的自动视频对象分割

在新一代MPEG－4视频编码标准中，为了支持面向对象编码和实现基于内容的应用，视频对象（VO）的自动分割成为关键技术之一，减背景法是视频对象自动分割的基本方法，但是不同的环境光照条件常常给视频对象的分割带来困难，提出一种基于统计推断的减背景法。该方法首先建立背景统计模型，然后对后续帧进行假设检验，从而分割出视频对象。文中算法采用HSV颜色空间，通过对背景统计模型中各颜色分量的有效分析和区别使用，能够很好地适应不同的环境光照条件。实验表明，文中算法能够在各种光照环境下自动地实现视频对象的准确分割。     

基于H．264压缩域的实时运动对象分割算法

在压缩域内直接分割运动对象对于有实时要求的应用而言是十分必要的，H．264以其优越的压缩效率已经在许多应用中逐渐取代了MPEG-2／4，但有关在H．264压缩域内进行运动对象分割的研究还很少。为此提出了一种从H.264压缩域实时分割运动对象的算法，该算法首先对从H．264视频中提取出的原始运动矢量场进行时域和空域的归一化，接着通过对连续多帧的运动矢量场进行累积来增强显著的运动信息；然后对累积运动矢量场进行全局运动补偿，同时利用快速的统计区域生长算法按照运动相似性将其分割成多个区域；最后利用运动矢量场的方向角直方图来判断出属于运动对象的分割区域，以组成运动对象。通过对多个MPEG-4测试序列的实验结果表明，该方法不仅能够从H．264压缩域中实时地分割出运动对象，且具有良好的分割质量。     

Multiresolution video object extraction fitted to scalable wavelet-based object coding

To enable content based functionalities in video processing algorithms, decomposition of scenes into semantic objects is necessary. A semi-automatic Markov random field based multiresolution algorithm is presented for video object extraction in a complex scene. In the first frame, spatial segmentation and user intervention determine objects of interest. The specified objects are subsequently tracked in successive frames and newly appeared objects/regions are also detected. The video object extraction algorithm includes discrete wavelet transform decomposition multiresolution Markov random field (MRF)-based spatial segmentation with emphasis on border smoothness at different resolutions, and an MRF-based backward region classification that determines the tracked objects in the scene. Finally, a motion constraint, embedded in the region classifier, determines the newly appeared objects/regions and completes the proposed algorithm towards an efficient video segmentation algorithm. The results are applicable for generic segmentation applications, however the proposed multiresolution video segmentation algorithm supports scalable object-based wavelet coding in particular. Moreover, compared to traditional object extraction algorithms, it produces smoother and more visually pleasing shape masks at different resolutions. The proposed effective multiresolution video object extraction method allows for larger motion, better noise tolerance and less computational complexity     

使用修改的豪氏道夫距离自动提取运动对象

新的视音频编码标准MPEG－4增加了支持基于内容的功能,它把视频序列分割成语义意义上的视频对象（VO）视频对象在某一瞬时的：“快照”称为视频对象平面（VOP）,且一系列VOP表示一个运动对象,VOP分割相当困难,这主要是因为物理对象通常不以亮度,彩色或光流等低级特征来表达,所以经典的分割方法无法获得有意义的分割结果,为了对这种视频运动图象进行有效的提取,提出了一种基于修改的豪氏道夫对象踊跃器的自动VOP分割方法,首先提取出初始模型,然后用跟踪器在序列中继帧中跟踪此对象,再对模型逐帧修改,以适应对象在后继帧中形状的旋转和变化,最后根据一系列二值模型来提取出视频对象,此外,为了提高分割效果帮减少复杂性,还使用了静 背景滤除技术来滤除静态背景,实验结果表明,该算法是有效的。     

电视图象中目标检索方法

本文给出一种有效的图象目标检索方法。该方法由颜色分割和定位算法组成。颜色分割中利用样本学习和非样本交互操作，提取目标的特征颜色，并以此建立颜色索引表，以提高颜色分割速度。这位算法中利用映射技术与区域相结合的操作确定目标的大小和位置。实验结果表明，本检索算法具有正确检测和定位目标的能力。     

结合识别信息的多目标视频分割

针对实际应用中待分割目标类型已知的情况,提出一种结合识别信息的多目标视频分割算法,使用训练数据集构建目标以及背景的特征字典,计算视频帧的超像素,构造一个分层条件随机场模型,用于约束视频帧的局部邻域和全局邻域,通过求解分层条件随机场模型,获得最终分割结果。实验结果表明,该算法能够对视频中相互遮挡及残缺不全的多个目标进行有效分割。     

结合识别信息的多目标视频分割

针对实际应用中待分割目标类型已知的情况,提出一种结合识别信息的多目标视频分割算法,使用训练数据集构建目标以及背景的特征字典,计算视频帧的超像素,构造一个分层条件随机场模型,用于约束视频帧的局部邻域和全局邻域,通过求解分层条件随机场模型,获得最终分割结果。实验结果表明,该算法能够对视频中相互遮挡及残缺不全的多个目标进行有效分割。     

Effective gaussian mixture learning for video background subtraction

Adaptive Gaussian mixtures have been used for modeling nonstationary temporal distributions of pixels in video surveillance applications. However, a common problem for this approach is balancing between model convergence speed and stability. This paper proposes an effective scheme to improve the convergence rate without compromising model stability. This is achieved by replacing the global, static retention factor with an adaptive learning rate calculated for each Gaussian at every frame. Significant improvements are shown on both synthetic and real video data. Incorporating this algorithm into a statistical framework for background subtraction leads to an improved segmentation performance compared to a standard method.     

基于小波变换和ICA的运动目标分割

提出一种时空融合的运动目标分割方法.在时域方面,采用时间轴一维小波变换提取运动对象,然后用独立成分分析法提取独立的运动对象,并基于灰度直方图进一步提取视频对象;在空域方面,提出对轮廓提取后的图像进行分水岭变换的改进方法.与COST211 AM算法比较表明,文中方法能更完整、准确地提取出运动对象.     

基于视频对象的视频内容分级描述模型

本文提出了一种基于视频对象的视频内容分级描述模型.视频序列首先被分成一个个的镜头,在每个镜头内对视频对象进行分割和跟踪.按照镜头、视频对象、视频对象平面和视频对象区域四级框架提取特征,对视频内容进行分级描述.本文对视频内容信息的描述可以用于视频检索、视频注释等应用.     

基层层次光流的半自动时空视频分割技术

在新一代MPEG-4视频编码标准中，为了支持面向对象编码和实现基于内容的应用，视频的半自动分割成为关键技术之一，为此提出了一种基于层次光流的半自动时空视频分割算法。该算法由空域分割和时域分割组成。在空域分割中，提出的基于点的图形用户界面（PBGUI），在用户的协助下，能够精确地定义需要分割的视频对象（VO）。时域分割根据空域分割的结果采用层次光流算法对视频对象进行边界和整体跟踪。实验结果表明，利用该算法，能够精确地分割出视频对象。     

基于层次光流的半自动时空视频分割技术

在新一代 MPEG- 4视频编码标准中 ,为了支持面向对象编码和实现基于内容的应用 ,视频的半自动分割成为关键技术之一 ,为此提出了一种基于层次光流的半自动时空视频分割算法 .该算法由空域分割和时域分割组成 .在空域分割中 ,提出的基于点的图形用户界面 (PBGU I) ,在用户的协助下 ,能够精确地定义需要分割的视频对象 (VO) .时域分割根据空域分割的结果采用层次光流算法对视频对象进行边界和整体跟踪 .实验结果表明 ,利用该算法 ,能够较精确地分割出视频对象 .     

一种基于细胞神经网络的视频运动对象分割的研究

针对目前大多数视频分割算法难以满足实时性要求的缺点,本文将具有很强并行处理能力并具有集成特性的CNN细胞神经网络应用到视频对象分割当中,提出了一种基于细胞神经网络的视频运动对象分割算法,并通过仿真实验验证了其可行性.     

Moving object detection using statistical background subtraction in wavelet compressed domain

Moving object detection is a fundamental task and extensively used research area in modern world computer vision applications. Background subtraction is one of the widely used and the most efficient technique for it, which generates the initial background using different statistical parameters. Due to the enormous size of the video data, the segmentation process requires considerable amount of memory space and time. To reduce the above shortcomings, we propose a statistical background subtraction based motion segmentation method in a compressed transformed domain employing wavelet. We employ the weighted-mean and weighted-variance based background subtraction operations only on the detailed components of the wavelet transformed frame to reduce the computational complexity. Here, weight for each pixel location is computed using pixel-wise median operation between the successive frames. To detect the foreground objects, we employ adaptive threshold, the value of which is selected based on different statistical parameters. Finally, morphological operation, connected component analysis, and flood-fill algorithm are applied to efficiently and accurately detect the foreground objects. Our method is conceived, implemented, and tested on different real video sequences and experimental results show that the performance of our method is reasonably better compared to few other existing approaches.

     

自动分割视频序列中运动物体的新算法：多特征联合方法

视频编码标准MPEG-4增加了适于多种应用的基于视频内容的功能,为了支持这一功能和提高编码效率,MPEG-4将视频序列中的每一帧分解成视频对象面(VOP);另外,由于基于内容的视频检索和视频监控系统均期望用分割出的关键视频对象紧致地表示一个序列,同时由于视频分割技术在模式识别、计算机视觉等领域也得到了广泛的应用,因此,分割视频运动物体并跟踪运动物体的变化变得至关重要.为了对视频中运动物体进行有效的分割,在帧差图象的基础上,采用Canny边缘检测和随机信号的高阶矩检测相结合的方法,来自动分割视频序列的前景区域和背景区域,并在前景区域中应用区域生长法进行颜色分割,以精确提取运动物体的边缘;还利用边缘和颜色特征来对分割出的运动物体建立模板,用于解决非刚体运动中局部暂时停止运动的情况.实验结果表明,此方法可以有效地分割运动物体,并能跟踪运动物体的变化.     

A framework for unsupervised mesh based segmentation of moving objects

Multimedia analysis usually deals with a large amount of video data with a significant number of moving objects. Often it is necessary to reduce the amount of data and to represent the video in terms of moving objects and events. Event analysis can be built on the detection of moving objects. In order to automatically process a variety of video content in different domain, largely unsupervised moving object segmentation algorithms are needed. We propose a fully unsupervised system for moving object segmentation that does not require any restriction on the video content. Our approach to extract moving objects relies on a mesh-based combination of results from colour segmentation (Mean Shift) and motion segmentation by feature point tracking (KLT tracker). The proposed algorithm has been evaluated using precision and recall measures for comparing moving objects and their colour segmented regions with manually labelled ground truth data. Results show that the algorithm is comparable to other state-of-the-art algorithms. The extracted information is used in a search and retrieval tool. For that purpose a moving object representation in MPEG-7 is implemented. It facilitates high performance indexing and retrieval of moving objects and events in large video databases, such as the search for similar moving objects occurring in a certain period.     

Fuzzy segmentation of video shots using hybrid color spaces and motion information

Video segmentation can be defined as the process of partitioning video into spatio-temporal objects that are homogeneous in some feature space, with the choice of features being very important to the success of the segmentation process. Fuzzy segmentation is a semi-automatic region-growing segmentation algorithm that assigns to each element in an image a grade of membership in an object. In this paper, we propose an extension of the multi-object fuzzy segmentation algorithm to segment pre-acquired color video shots. The color features are selected from channels belonging to different color models using two different heuristics: one that uses the correlations between the color channels to maximize the amount of information used in the segmentation process, and one that chooses the color channels based on the separation of the clusters formed by the seed spels for all possible color spaces. Motion information is also incorporated into the segmentation process by making use of dense optical flow maps. We performed experiments on synthetic videos, with and without noise, as well as on some real videos. The experiments show promising results, with the segmentations of real videos produced using hybrid color spaces being more accurate than the ones produced using three other color models. We also show that our method compares favorably to a state-of-the art video segmentation algorithm.     

基于时空图的轮廓线模型研究

主动轮廓线模型(Snakes)具有能够结合先验知识和图像特征的理论价值,但由于能量局部极值问题的困扰,而鲜已用于实际问题。前人通过改进优化算法来提高其分割效果。文章则尝试从Snakes的外部力入手,引入时空图的光流特征,通过改造能量函数提高分割效果。实验对比验证在复杂背景下,基于时空图的轮廓线模型依然可以有效跟踪运动物体,此时普通Snakes模型必须做大量手动调整。而它同时也解决了帧差等视频分割方法难于识别、提取目标轮廓的问题。时空图轮廓线模型抓住了运动目标的图像特征,在简单的优化算法下依然可以得到较好的分割效果,可以用实时系统。