A motion and illumination resilient framework for automatic shot boundary detection

Detecting and locating a desired information in hefty amount of video data through manual procedure is very cumbersome. This necessitates segregation of large video into shots and finding the boundary between the shots. But shot boundary detection problem is unable to achieve satisfactory performance for video sequences consisting of flash light and complex object/camera motion. The proposed method is intended for recognising abrupt boundary between shots in the presence of motion and illumination change in an automatic way. Typically any scene change detection algorithm assimilates time separation in a shot resemblance metric. In this communication, absolute sum gradient orientation feature difference is matched to automatically generated threshold for sensing a cut. Experimental study on TRECVid 2001 data set and other publicly available data set certifies the potentiality of the proposed scheme that identifies scene boundaries efficiently, in a complex environment while preserving a good trade-off between recall and precision measure.     

一种自动选取阈值的视频镜头边界检测算法

镜头边界检测是实现基于内容的视频检索的一个重要步骤.文中介绍了现有的镜头边界检测的基本方法,并针对其不足提出了一种自动选取阈值的、综合考虑颜色和空间特征的镜头边界检测算法.该方法能较好地检测出镜头突变和物体运动以及光线变化等情况,对渐变镜头也能达到检测的目的.实验结果表明算法能够有效地检测出视频镜头边界.     

基于累积直方图的视频镜头边界检测方法

提出了一种基于累积直方图的视频镜头边界检测方法,以累积直方图来代表视频帧图像的特征,其帧差充分反映了视频帧图像间的差异性;结合滑动窗的局部阈值分割处理,获得镜头边界检测对物体/摄像机的运动和光线变化的不敏感性。实验结果表明,本方法在镜头突变边界检测中达到95.97%的查全率和96.75%的查准率。     

一种基于模糊逻辑的MPEG压缩视频场景转换检测方法

镜头边界的自动检测是实现基于内容的视频检索必不可少的第一步,目前大多数的场景转换检测方法都是基于非压缩视频的,而越来越多的视频数据却以压缩形式存在。本主文提出了一咱新的针对ＭＰＥＧ压缩视频的场景转换检测算法,它利用ＤＣ序列和运动向量计算像素差、直方图差、统计差和具有“真实”运动向量的宏块所占的比例,然后用模糊逻辑对上述参量加以综合隶属度用自适应的方法确定。实验表明这种镜头检测算法具有较高的检出率和     

镜头变换边界复合检测算法研究

将视频图像进行有效时间分段及标定是视频图像内容分析、随机接入、视频数据库浏览与检索的重要研究内容。在对视频图像进行时间分段和标定过程中,镜头变换边界的自动检测十分重要。镜头变换边界的自动检测算法在国际上得到了广泛的研究,在分析了当前国际上的镜头变换边界检测算法的基础上,提出了一种镜头变换边界复合检测算法。理论分析及实验结果表明该复合检测算法不仅具有实现复杂度较低,而且对各类型图像序列检测性能好的特点。     

压缩域中基于支持向量机的镜头边界检测算法

针对如何进一步提高镜头边界检测精度问题,本文提出了一个基于支持向量机SVM (Support Vector Machine)的镜头边界检测算法.该算法利用视频压缩域中特征,如宏块类型,帧间对应宏块DC系数差和帧类型将视频帧分为发生切变的帧、发生渐变的帧和非镜头变换帧三类,从而实现视频的镜头分割.实验结果表明该算法对摄像机的运动和大物体的进入具有很好的鲁棒性,且没有大多数算法中阈值选择的困难,将我们的算法与2001 TREC评估中最佳指标进行了比较,在综合度量查全率和查准率的性能指标F1上,比2001 TREC评估中最佳指标高约8％.     

一种快速有效的视频镜头边界检测方法

本文在分析现有的镜头边界检测方法的基础上,提出了一种层次化镜头边界检测方法.首先我们利用小波变换对信号奇异点的检测能力和对噪声具有较强的鲁棒性,实现了镜头边界的预检测.然后我们通过对候选镜头边界的虚警分析,有效地降低了闪光灯和快速运动对镜头边界检测产生的影响,并实现了镜头突变检测.在渐变类型分类中,我们分别实现了淡入淡出、扫换和溶解等三种重要镜头边界类型的检测.实验结果表明,本文方法能够快速、有效地实现视频镜头边界类型的检测,对运动和光线变化有较强的鲁棒性.     

一种嵌入H.264/AVC的镜头切变检测算法

提出一种嵌入H.264/AVC的视频镜头切变检测算法,利用视频压缩过程中运动估计与补偿的结果,提高了检测的效率,大大降低了检测的运算量;同时,为了针对镜头内可能出现的噪声帧,利用H.264/AVC标准中的多参考帧特性,进一步提出了镜头内噪声帧或噪声序列段的检测窗口算法.     

A new pyramidal opponent color-shape model based video shot boundary detection

Video shot boundary detection (VSBD) is one of the most essential criteria for many intelligent video analysis-related applications, such as video retrieval, indexing, browsing, categorization and summarization. VSBD aims to segment big video data into meaningful fragments known as shots. This paper put forwards a new pyramidal opponent colour-shape (POCS) model which can detect abrupt transition (AT) and gradual transition (GT) simultaneously, even in the presence of illumination changes, huge object movement between frames, and fast camera motion. First, the content of frames in the video subjected to VSBD is represented by the proposed POCS model. Consequently, the temporal nature of the POCS model is subjected to a suitable segment (SS) selection procedure in order to minimize the complexity of VSBD method. The SS from the video frames is examined for transitions within it using a bagged-trees classifier (BTC) learned on a balanced training set via parallel processing. To prove the superiority of the proposed VSBD algorithm, it is evaluated on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of video according to no transition (NT), AT and GT. The experimental evaluation results in an F₁-score of 95.13%, 98.13% and 97.11% on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets, respectively.     

Shot boundary detection in the presence of fire flicker and explosion using stationary wavelet transform

Detection of fire in video for fire alarm systems has been studied by many researchers, but detection of shot boundaries under fire, flicker and explosion (FFE) is one of the under-studied areas. In thriller movies, FFE occur more often than other special effects and lead to false detection of shot boundary. We tested major metrics used for detection of shot boundaries under FFE for various movies. It is observed that for almost all metrics, precision is low due to false positives caused by FFE. We propose an algorithm based on cross-correlation coefficient, stationary wavelet transform and combination of local and adaptive thresholds for detection of shot boundaries under FFE. The proposed algorithm is tested on three movies, and experimental results validate the effectiveness of our proposed method in terms of better recall and precision.     

A novel moving object segmentation framework utilizing camera motion recognition for H.264 compressed videos

This paper presents a novel coarse to fine moving object segmentation framework for H.264/AVC compressed videos. The proposed framework integrates the global motion estimation and global motion compensation steps in the segmentation pipeline unlike previous techniques which did not consider such an integration. The integration is based on testing for presence of global motion by classifying the interframe motion vectors into moving camera class and still camera class. The decision boundary separating these two classes is learnt from the training video data. The integration automates the moving object segmentation to be applicable for static, moving and combination of static/moving camera cases which to the best of our knowledge has not been carried out earlier. Further, a novel coarse segmentation technique is proposed by decomposing the inter-frame motion vectors into wavelet sub-bands and utilizing logical operations on LH, HL and HH sub-band wavelet coefficients. The premise is based on the fact that since the LH, HL and HH sub-bands contain the detail information pertaining to horizontal, vertical and diagonal moving blocks respectively, they can be exploited to identify the coarse moving boundaries. The coarse segmentation is fast in comparison to state-of-the-art coarse segmentation methods as demonstrated by our experiments. Finally, these coarse boundaries are modeled in an energy minimization framework and shown that by minimizing the energy using graph cut optimization the segmentation is refined to obtain the fine segmentation. The proposed framework is tested on a number of standard video sequences encoded with H.264/AVC JM encoder and comparison is carried out with state-of-the-art compressed domain moving object segmentation methods as well as with an existing state-of-the-art pixel domain method to establish and validate the proposed moving object segmentation framework.     

Compressed video processing for cut detection

One of the challenging problems in video databases is the organisation of video information. Segmenting a video into a number of clips and characterising each clip has been suggested as one mechanism for organising video information. This approach requires a suitable method to automatically locate cut points (boundaries between consecutive camera shots in a video). Several existing techniques solve this problem using uncompressed video. Since video is increasingly being captured, moved, and stored in compressed form, there is a need for detecting shot boundaries directly in compressed video. The authors address this issue and show certain feature extraction steps in MPEG compressed video that allow the implementation of most of the existing cut detection methods developed for uncompressed video for MPEG video stream. They also examine the performance of three tests for cut detection by viewing the problem of cut detection as a statistical hypothesis testing problem. As the experimental results indicate, the statistical hypothesis testing approach permits fast and accurate detection of video cuts     

Fast perceptual region tracking with coding-depth sensitive access for stream transcoding

Object-based bit allocation can result in significant improvement in the perceptual quality of extremely compressed video. However, real-time video object detection in large format high fidelity video is computationally daunting. Most algorithms begin with extensive use of classical bit analysis, and thus remain computationally heavy. Based on some recent results in human visual perception, in this paper, we present an experimental visual region tracking algorithm particularly designed for perceptual stream transcoding. This exploits the cue order observed in human visual perception to achieve very high computation speed as well as tracking efficiency. Rather than begin processing from pixel level or using any pixel level processing at all, it employs high level motion cue and block shape cue analysis to identify signatures of various relative movements between object of interest, scene background and the camera on the motion vector set, and from there it identifies objects. It then uses predictive filters to track the regions. The result is a fast yet highly effective perceptual region tracking algorithm that can operate in stream rate and track regions of perceptually significant object despite camera movements such as zoom, panning and translation. The technique is not specific to any special class of objects. We have implemented this algorithm in a live ISO-13818/MPEG-2 perceptual transcoder. In this paper, we share the performance of this implementation. This fast object-aware video rate transcoder is particularly suitable for live streaming and can convert a regular stream into a perceptually coded video stream.     

一种MPEG压缩视频的镜头边界检测算法

提出一种工作在MPEG压缩域的检测算法.首先从压缩视频中提取DC图像和重构参考帧,然后经过全局运动补偿之后进行基于边对象变化率的镜头分割点检测,最后结合DC图直方图差法构成联合检测算法.本算法能准确地检测到镜头渐变,镜头切变,能得到渐变类型等特性.用AdobePremiere5.1生成的各类镜头转换视频片段进行检测,实验结果验证了算法的有效性.     

基于窗帧差的镜头边界系数模型及其应用

针对视频结构分析提出了一种新的镜头边界系数模型.首先,按照特定的计算方法计算当前帧的窗帧差,使得窗帧差在镜头内与镜头边界呈现不同的特征;然后,采用相应的镜头边界检测算子计算镜头边界相似系数,镜头边界相似系数越大,则该帧越可能是镜头边界;最后,为了提高镜头边界相似系数的检测能力,根据镜头边界相似系数定义了镜头边界系数.镜头边界系数具有良好的特性,可以单独检测镜头边界,也可以与传统的镜头边界检测方法相结合,为镜头边界检测提供了一种鲁棒的框架.实验结果表明,基于镜头边界系数模型的镜头边界检测方法能明显改善镜头边界检测结果.     

Video Shot Boundary Detection in MPEG Compressed Sequences Using SVM Learning

A number of automated video shot boundary detection methods for indexing a videosequence to facilitate browsing and retrieval have been proposed in recent years.Among these methods,the dissolve shot boundary isn't accurately detected because it involves the camera operation and objectmovement.In this paper,a method based on support vector machine (SVM) is proposed to detect thedissolve shot boundary in MPEG compressed sequence.The problem of detection between the dissolveshot boundary and other boundaries is considered as two-class classification in our method.Featuresfrom the compressed sequences are directly extracted without decoding them,and the optimal classboundary between two classes are learned from training data by using SVM.Experiments,whichcompare various classification methods,show that using proposed method encourages performance ofvideo shot boundary detection.     

基于模糊聚类的运动对象分割算法研究

为了从视频序列中分割出完整的、一致的运动视频对象,该文使用基于模糊聚类的分割算法获得组成对象边界的像素,从而提取对象。该算法首先使用了当前帧以及之前一些帧的图像信息计算其在小波域中不同子带的运动特征,并根据这些运动特征构造了低分辨率图像的运动特征矢量集;然后,使用模糊C-均值聚类算法分离出图像中发生显著变化的像素,以此代替帧间差图像,并利用传统的变化检测方法获得对象变化检测模型,从而提取对象;同时,使用相继两帧之间的平均绝对差值大小确定计算当前帧运动特征所需帧的数量,保证提取视频对象的精确性。实验结果证明该方法对于分割各种图像序列中的视频对象是有效的。     

双层特征优化的视觉运动目标跟踪算法

视觉监控中运动目标跟踪容易受到遮挡、目标快 速运动与外观变化等因素的素影响,单层特征难以有 效解决这些问题。为此,提出一种像素级与区域级特征组合优化的视觉跟踪算法。首 先在像素级利用 目标和背景区域颜色特征的后验概率对目标与背景进行初步判别;然后对候选区域进行超像 素分割,并依据 像素级的判断结果,在超像素区域内利用投票决策模型对目标与背景信息进行统计分析,得 到精确的目标位 置分布;最后结合均值漂移迭代搜索得到目标的准确位置,并利用双层判别结果对目标跟踪 过程的遮挡情况 进行检测,同时动态更新目标以及背景区域信息以适应目标外观与场景变化。与典型算法进 行对比的实验结 果表明,本文算法能够有效应对目标遮挡与快速运动等因素的影响,适用于复杂场景条件下 实时的运动目标跟踪。     

Temporal video segmentation: A survey

Temporal video segmentation is the first step towards automatic annotation of digital video for browsing and retrieval. This article gives an overview of existing techniques for video segmentation that operate on both uncompressed and compressed video stream. The performance, relative merits and limitations of each of the approaches are comprehensively discussed and contrasted. The gradual development of the techniques and how the uncompressed domain methods were tailored and applied into compressed domain are considered. In addition to the algorithms for shot boundaries detection, the related topic of camera operation recognition is also reviewed.