基于内容的视频分割技术研究

随着MPEG-4基于内容功能的提出以及MPEG-7标准的不断推广应用,视频对象分割技术已成为视频处理领域中的研究热点。视频对象分割就是从视频序列中分割出在语义上有意义的对象。目前对视频分割研究已从基于镜头的分割发展到了基于内容的视频对象分割。然而,基于内容的视频对象分割技术还不成熟。文中讨论了视频对象分割技术的发展和研究状况,从组成视频运动对象的分割系统出发,介绍了时域分割以及时空域联合分割等技术,并提出了一种基于多帧差的视频对象分割算法。最后对分割技术中需要深入研究的问题进行了探讨。     

基于内容的视频分割技术研究

随着MPEG-4基于内容功能的提出以及MPEG-7标准的不断推广应用，视频对象分割技术已成为视频处理领域中的研究热点。视频对象分割就是从视频序列中分割出在语义上有意义的对象。目前对视频分割研究已从基于镜头的分割发展到了基于内容的视频对象分割。然而，基于内容的视频对象分割技术还不成熟。文中讨论了视频对象分割技术的发展和研究状况，从组成视频运动对象的分割系统出发，介绍了时域分割以及时空域联合分割等技术，并提出了一种基于多帧差的视频对象分割算法。最后对分割技术中需要深入研究的问题进行了探讨。     

交互式分割视频运动对象的研究与实现

在 MPEG- 4视频编码标准中 ,为了实现基于视频内容的交互功能 ,视频序列的每一帧由视频对象面来表示 ,而生成视频对象面 ,需要对视频序列中运动对象进行有效分割 ,并跟踪运动对象随时间的变化 .在视频分割方法中 ,交互式分割视频对象能满足分割的效率与质量指标要求 ,因此提出了一种交互分割与自动跟踪相结合的方式来分割视频语义对象 ,即在初始分割时 ,依据用户的交互与形态学的分水线分割算法相结合提取视频对象轮廓 ,并用改进的轮廓跟踪方法有效提高视频对象轮廓的精度 ;对后续帧的跟踪 ,采用六参数仿射变换跟踪运动对象轮廓的变化 ,用平移估算的运动矢量作为初始值 ,计算六参数仿射变换的参数 .实验结果表明 ,该方法能有效地分割并跟踪视频运动对象     

基于内容检索的视频分割技术的研究

视频分割就是从视频序列中分割出在语义上有意义的对象。目前，视频分割已从基于镜头的分割发展到了基于内容的视频对象分割。文章介绍了基于内容的时域及空域视频分割技术，提出了一种基于多帧差异的视频对象分割算法。     

基于时空信息的视频对象分割算法及其在细胞神经网络机上的实现

随着MPEG-4和MPEG-7的研究发展,基于内容编码和面向对象的存取和操纵技术日益得到人们的重视,视频分割技术迅速成为当前视频研究领域的热点.为了能够实时准确地对运动视频对象进行分割,本文提出了一种时空结合的视频分割算法,即先利用帧间差分求出大致的视频对象,经过投影定位,再通过灰度连通区域的标记对其进行修正.本算法直接面对灰度图象序列处理,并且综合和时间空间上的信息,使得对于运动对象的分割更加准确和有效,同时由于并行的细胞神经网络的介入,使得算法具有更好的实时性.通过在细胞神经网络机CNNUM上的模拟结果证明,利用该算法,能够较精确地分割出视频对象.     

视频分割技术的发展

随着MPEG－4和MPEG－7的研究发展以及最近几年数字视频图书馆技术的崛起,基于内容编码和面向对象的存取和操纵技术日益受到人们的重视,视频分割技术迅速成为当前视频研究领域的热点。视频分割是新一代视频编码、视频检索、互联网多媒体交互等新兴领域的关键技术。介绍了视频分割的主要技术,对其算法和性能进行了比较和评述,并分析了当前视频分割技术的研究现状、尚存在的问题和研究前景。     

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。     

一种基于时空联合的视频对象分割方法

视频对象分割在基于内容的视频编码和视频检索中均有重要的应用.为此,针对视频对象分割,提出了一种时域和空域信息融合的视频对象分割方案,该方案首先对时域分割采用基于F-假设检验的方法来得到初始的变化检测模板,然后通过与基于形态学的空域分割融合来获得最终的运动对象.实验结果表明,该方案计算比较简单,能较好地将前景运动对象从静止或运动、简单或复杂的背景中分离出来,且定位精度较好.     

灰度投影结合Fisher准则的对象分割

视频对象分割是基于内容的视频编码和视频检索中的的对象跟踪方法,提出了一种可以从复杂场景中分割出MPEG-4的视频对象新方法.首先采用灰度投影匹配进行全局运动估计和补偿,用以消除背景变化的影响;然后由二次差分抽取中间帧解决遮挡问题,通过Fisher评价函数结合数学形态学填充得到运动对象分割掩膜,同时消除残余噪声以及平滑边缘.实验结果表明,该方法在一定范围内较好地解决了遮挡问题,并能够高效快速地得到比较精确的视频对象.     

以统计变化检测为基础的实时分割视频对象新方法

为了克服利用变化检测分割视频对象过程中的噪声、复杂运动、暴露背景的影响，提出了一种基于统计变化检测的实时分割视频对象新方法。在该方法中，由于统计变化检测技术是利用t分布来有效消除噪声的影响，而不需要估计噪声的方差，而且可利用间隔为k的两帧图像代替连续两帧来进行变化检测，因此可以很好地处理关节运动和慢运动；另外，对两个连续的统计变化检测结果取交集还可以消除暴露背景的影响，并能消除大部分的残留噪声，且几乎不增加计算量，因此统计变化检测可作为视频对象分割的基础，试验结果表明，该方法不仅解决了传统的变化检测过程中的噪声、复杂运动以及暴露背景影响，而且能够自动实时地分割视频对象，以满足MPEG-4等基于对象的视频应用。     

Activity based surveillance video content modelling

This paper tackles the problem of surveillance video content modelling. Given a set of surveillance videos, the aims of our work are twofold: firstly a continuous video is segmented according to the activities captured in the video; secondly a model is constructed for the video content, based on which an unseen activity pattern can be recognised and any unusual activities can be detected. To segment a video based on activity, we propose a semantically meaningful video content representation method and two segmentation algorithms, one being offline offering high accuracy in segmentation, and the other being online enabling real-time performance. Our video content representation method is based on automatically detected visual events (i.e. ‘what is happening in the scene’). This is in contrast to most previous approaches which represent video content at the signal level using image features such as colour, motion and texture. Our segmentation algorithms are based on detecting breakpoints on a high-dimensional video content trajectory. This differs from most previous approaches which are based on shot change detection and shot grouping. Having segmented continuous surveillance videos based on activity, the activity patterns contained in the video segments are grouped into activity classes and a composite video content model is constructed which is capable of generalising from a small training set to accommodate variations in unseen activity patterns. A run-time accumulative unusual activity measure is introduced to detect unusual behaviour while usual activity patterns are recognised based on an online likelihood ratio test (LRT) method. This ensures robust and reliable activity recognition and unusual activity detection at the shortest possible time once sufficient visual evidence has become available. Comparative experiments have been carried out using over 10 h of challenging outdoor surveillance video footages to evaluate the proposed segmentation algorithms and modelling approach.     

基于C3D的足球视频场景分类算法

足球视频整场比赛持续时间较长,许多视频内容并非广大观众的兴趣所在,因此足球视频场景分类成为了近几十年来研究界的一项重要课题,许多机器学习方法也被应用于这个课题上.本文提出的基于C3D （三维卷积神经网络）的足球视频场景分类算法,将三维卷积运用于足球视频领域,并通过实验验证了本文算法的可行性.本文实验的流程如下：首先,基于帧间差分法和徽标检测法检测法对足球视频场景切换进行检测,实现镜头分割.在此基础上,提取分割镜头的语义特征并将其进行标记,然后通过C3D对足球事件进行分类.本文将足球视频分为7类,分别为远镜头、中镜头、特写镜头、回放镜头、观众镜头、开场镜头及VAR （视频助理裁判）镜头.实验结果表明,该模型在足球视频数据集上的分类准确率为96%.     

Automatic video temporal segmentation based on multiple features

This paper investigates automatic video temporal segmentation techniques, also named shot boundary detection (SBD) techniques. Firstly, the existing SBD algorithms are reviewed in detail. Then, a new SBD algorithm is proposed aiming to obtain fast and accurate detection, and its performances are evaluated and compared with existing works. This algorithm computes the frame difference/similarity by such simple features as pixel difference and histogram difference, adopts motion-based difference to resist camera or object movements in the same shot and uses the flash detection to avoid false positives caused by light changes or flashes. The adopted features are computational efficient, and the combination of various features improve the detection accuracy. These properties make the algorithm suitable for real-time applications, such as broadcasted news segmentation.     

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.     

足球视频的结构分析与概要

该文描述了一种有效的框架对足球视频进行结构分析,根据电影特征和对象特征生成视频概要。由于足球视频的特殊性,本文在镜头边界检测中采用分层检测的方法:象素点对的比较、颜色直方图和对象分割和跟踪技术。我们在镜头分类中对中远镜头的区分提出了新的方法。以慢动作回放镜头为标志,通过分析镜头间的关联规则生成视频概要。     

Novel automatic video cut detection technique using Gabor filtering

Video shot transition identification constitutes an important computer vision research field, being applied, as an essential step, in many other digital video analysis domains: video scene detection, video compression, video indexing, video content retrieval and video object tracking. This paper approaches the video cut transition detection domain, providing a novel feature-based automatic identification method. We propose a feature extraction technique that uses 2D Gabor filtering, computing tridimensional image feature vectors for the video frames. Most shot cut detection techniques use a thresholding operation to discriminate between the inter-frame difference metric values and thus identify the video break points. Our identification approach is not threshold-based, using an automatic unsupervised distance classification procedure instead of a threshold. Thus, we provide a region-growing based classification approach, that proves to be very efficient in clustering the distances between feature vectors of consecutive frames. The two resulted distance classes determine a satisfactory video shot detection.     

视频镜头时域分割方法的研究

视频时域分割指将视频序列分成若干镜头，是视频内容分析以及基于内容的视频浏览和检索的第一步．该文首先对视频结构以及视频镜头种类进行了简要的描述，然后对为计算不连续性而采用的提取特征和建立测量准则的常用方法进行概述．其后，文章介绍了检测镜头切变和渐变的算法及其优缺点．在压缩域上检测镜头变换边界的问题也在文中予以分析．在结论与展望中，提出了一些这一领域的难点和对今后工作的展望．     

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