一种基于模型的扫换检测方法

视频自动分割是实现视频数据库检索必不可少的一个过程,其基础是镜头边界检测.当前已有的算法能够较准确地检测出镜头突变,但对于镜头的渐变则常常会漏检,这是由于镜头渐变时帧间差没有一个明显的峰值,因而其检测比突变检测要困难得多.扫换是一种常用的视频空间编辑手段,用于实现多种镜头变化.通过分析各种类型的扫换,提出了一种新的基于视频空间编辑模型的扫换检测算法,其性能优于Alattar提出的基于统计特征的算法.对用AdobePremiere5.1生成的各种扫换视频进行检测.实验结果表明,这种扫换检测算法能够较好地适应     

Gaussian process motion graph models for smooth transitions among multiple actions

We propose a unified model for human motion prior with multiple actions. Our model is generated from sample pose sequences of the multiple actions, each of which is recorded from real human motion. The sample sequences are connected to each other by synthesizing a variety of possible transitions among the different actions. For kinematically-realistic transitions, our model integrates nonlinear probabilistic latent modeling of the samples and interpolation-based synthesis of the transition paths. While naive interpolation makes unexpected poses, our model rejects them (1) by searching for smooth and short transition paths by employing the good properties of the observation and latent spaces and (2) by avoiding using samples that unexpectedly synthesize the nonsmooth interpolation. The effectiveness of the model is demonstrated with real data and its application to human pose tracking.     

A shot detection technique using linear regression of shot transition pattern

Video segmentation acts as the fundamental step for various applications like, archiving, content based retrieval, copy detection and summarization of video data. Shot detection is first level of segmentation. In this work, a shot detection methodology is presented that evolves around a simple shot transition model based on the similarity of the frames with respect to a reference frame. Frames in an individual shot are very similar in terms of their visual content. Whenever a shot transition occurs a change in similarity values appears. For an abrupt transition, the rate of change is very high, while for gradual it is not so apparent. To overcome the effect of noise in similarity values, line is fit over a small window using a linear regression. Thus slope of this line exhibits the underlying pattern of transition. A novel algorithm for shot detection, hence, is developed based on the variation pattern of the similarity values of the frames with respect to a reference frame. First an algorithm is proposed, which is direct descendant of the underlying transition model and applies a threshold on the similarity values to detect the transitions. Then this algorithm is improved by utilizing the slope of linear approximation of variation in similarity values rather than the absolute values, following least square regression. Threshold on the slope is determined with a bias towards minimizing false rejection rate at the cost of false acceptance rate. Finally, a simple post-processing technique is adopted to reduce the false detection. Experiment is done with the video sequences taken from TRECVID 2001 database, action type movie video, recorded sports and news video. Comparison with few other systems indicates that the performance of the proposed scheme is quite satisfactory.     

Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. A novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.     

鲁棒的镜头边缘检测融合算法

对于镜头骤变检测，通过最小扩展树算法(MST)提取保留了视频帧空间信息的颜色聚合向量直方图、引入了动态自适应阈值和时间阻尼，使得骤变检测效率高；对于渐变检测，通过分析其数学产生模型，根据模型特性实现渐变镜头检测；最后，采用一个良好的融合策略同时完成骤变和渐变。     

An Accumulation Algorithm for Video Shot Boundary Detection

In this paper, an accumulation algorithm for video shot detection is introduced. The algorithm considers the properties of gradual transition. In a gradual transition, there is only a small difference between consecutive frames. The algorithm remembers the differences between consecutive frames and accumulates them. When the accumulation difference exceeds a threshold, an occurrence of shot transition is declared. Our main contributions are to introduce a frame C that remembers the changes from the beginning of a shot and detect the different types of boundaries (cut, fade, dissolve) at one process. We tested our algorithm with clips extracted from MPEG VCDs. The algorithm showed a good performance in detecting the gradual transitions as well as the abrupt cuts and has the ability to identify different types of boundaries.     

基于运动补偿和自适应双闭值的镜头分割

镜头分割是视频检索的结构化基础.提出一种高效的镜头分割算法,首先针对视频中的图像帧序列间存在一定的运动,对视频中的图像帧采用运动矢量场自适应搜索技术进行分块运动矢量估计,然后进行运动补偿,接着在此基础上计算两帧间的像素值不变点数,并基于滑动窗口获取局部自适应的双阈值,最后基于自适应双阈值间的比较检测切变和渐变.实验结果证实,该算法不仅能够有效地检测出镜头的切变和渐变,而且对运动具有较强的鲁棒性.     

Temporal segmentation of video using frame and histogram space

Two algorithms are presented for the detection of gradual transitions in video sequences. The first is a dissolve detection algorithm utilizing certain properties of a dissolve's trajectory in image-space. It is implemented both as a simple threshold-based detector, and as a parametric detector by modeling the error properties of the extracted statistics. The second is an algorithm to detect a wide variety of wipes based on image histogram characteristics during such transitions. Both algorithms operate in the compressed domain, requiring only partial decoding of the compressed video stream. Experiments show the algorithms perform as well as-and in some cases, better than-full-frame methods, on a wide variety of gradual transitions, and can operate significantly faster than real-time.     

基于三步筛选的视频渐变镜头检测

针对视频中的叠化与淡入淡出现象,提出一种基于三步筛选的渐变镜头检测算法。提取视频帧的亮度和方差作为特征,通过有限状态机实现初始渐变检测,并计算视频帧的颜色、共生矩阵、运动特征,从而进行三步筛选,保证检测的准确性。对TRECVID视频进行实验,结果表明,该算法对渐变具有较好的检测性能,对运动及闪光现象有较强的鲁棒性。     

4D Model Flow: Precomputed Appearance Alignment for Real‐time 4D Video Interpolation

We introduce the concept of 4D model flow for the precomputed alignment of dynamic surface appearance across 4D video sequences of different motions reconstructed from multi‐view video. Precomputed 4D model flow allows the efficient parametrization of surface appearance from the captured videos, which enables efficient real‐time rendering of interpolated 4D video sequences whilst accurately reproducing visual dynamics, even when using a coarse underlying geometry. We estimate the 4D model flow using an image‐based approach that is guided by available geometry proxies. We propose a novel representation in surface texture space for efficient storage and online parametric interpolation of dynamic appearance. Our 4D model flow overcomes previous requirements for computationally expensive online optical flow computation for data‐driven alignment of dynamic surface appearance by precomputing the appearance alignment. This leads to an efficient rendering technique that enables the online interpolation between 4D videos in real time, from arbitrary viewpoints and with visual quality comparable to the state of the art.     

A Shot boundary Detection Technique based on Visual Colour Information

The paper proposes a novel video segmentation system with maiden application of CIEDE2000 colour-difference and mean luminace pattern. CIEDE2000 colour-difference uses Lab colour space which is a stable and efficient colour space. The main advantage of Lab colour space model is that it can approximate all the available colours perceived by our human eye. CIEDE2000 colour difference is used for detecting abrupt transitions in the video. The novel contribution of the paper is the maiden use of the mean luminance pattern, increasing and decreasing patterns of the mean value of frame luminance, for detecting the gradual transition. The approach is validated on standard databases TRECVid 2001 and 2007 test video database. The performance of the proposed technique is compared with recently reported techniques and found to be superior as compared to other techniques. The accuracy achieved with the proposed method on the standard databases is 95.9% for cut transition, 78.6% for gradual transition and 92.1% overall.

     

Using string matching to detect video transitions

The detection of shot boundaries in videos captures the structure of the image sequences by the identification of transitional effects. This task is important in the video indexing and retrieval domain. The video slice or visual rhythm is a single two-dimensional image sampling that has been used to detect several types of video events, including transitions. We use the longest common subsequence (LCS) between two strings to transform the video slice into one-dimensional signals obtaining a highly simplified representation of the video content. We also developed a chain of mathematical morphology operations over these signals leading to the detection of the most frequent video transitions, namely, cut, fade, and wipe. The algorithms are tested with success with various genres of videos.     

Spatial-temporal joint probability images for video segmentation

Effective annotation and content-based search for videos in a digital library require a preprocessing step of detecting, locating and classifying scene transitions, i.e., temporal video segmentation. This paper proposes a novel approach—spatial-temporal joint probability image (ST-JPI) analysis for temporal video segmentation. A joint probability image (JPI) is derived from the joint probabilities of intensity values of corresponding points in two images. The ST-JPT, which is a series of JPIs derived from consecutive video frames, presents the evolution of the intensity joint probabilities in a video. The evolution in a ST-JPI during various transitions falls into one of several well-defined linear patterns. Based on the patterns in a ST-JPI, our algorithm detects and classifies video transitions effectively.Our study shows that temporal video segmentation based on ST-JPIs is distinguished from previous methods in the following way: (1) It is effective and relatively robust not only for video cuts but also for gradual transitions; (2) It classifies transitions on the basis of predefined evolution patterns of ST-JPIs during transitions; (3) It is efficient, scalable and suitable for real-time video segmentation. Theoretical analysis and experimental results of our method are presented to illustrate its efficacy and efficiency.     

ScribbleBoost: Adding Classification to Edge‐Aware Interpolation of Local Image and Video Adjustments

One of the most common tasks in image and video editing is the local adjustment of various properties (e.g., saturation or brightness) of regions within an image or video. Edge‐aware interpolation of user‐drawn scribbles offers a less effort‐intensive approach to this problem than traditional region selection and matting. However, the technique suffers a number of limitations, such as reduced performance in the presence of texture contrast, and the inability to handle fragmented appearances. We significantly improve the performance of edge‐aware interpolation for this problem by adding a boosting‐based classification step that learns to discriminate between the appearance of scribbled pixels. We show that this novel data term in combination with an existing edge‐aware optimization technique achieves substantially better results for the local image and video adjustment problem than edge‐aware interpolation techniques without classification, or related methods such as matting techniques or graph cut segmentation.     

A motion-tolerant dissolve detection algorithm

Gradual shot change detection is one of the most important research issues in the field of video indexing/retrieval. Among the numerous types of gradual transitions, the dissolve-type gradual transition is considered the most common one, but it is also the most difficult one to detect. In most of the existing dissolve detection algorithms, the false/miss detection problem caused by motion is very serious. In this paper, we present a novel dissolve-type transition detection algorithm that can correctly distinguish dissolves from disturbance caused by motion. We carefully model a dissolve based on its nature and then use the model to filter out possible confusion caused by the effect of motion. Experimental results show that the proposed algorithm is indeed powerful.     

Smooth Loops from Unconstrained Video

Converting unconstrained video sequences into videos that loop seamlessly is an extremely challenging problem. In this work, we take the first steps towards automating this process by focusing on an important subclass of videos containing a single dominant foreground object. Our technique makes two novel contributions over previous work: first, we propose a correspondence‐based similarity metric to automatically identify a good transition point in the video where the appearance and dynamics of the foreground are most consistent. Second, we develop a technique that aligns both the foreground and background about this transition point using a combination of global camera path planning and patch‐based video morphing. We demonstrate that this allows us to create natural, compelling, loopy videos from a wide range of videos collected from the internet.     

基于视频的现实人物运动创作

提出一种根据用户指定的人体运动和观察视角生成真实感视频的方法.首先采集演员进行少数基本运动时的多视角视频数据库,并使用无标记运动捕捉的方法获得任意时刻人体对应的骨骼和3D模型.其次,用户对人体骨架指定运动并设定视角,以此定义目标视频.实验结果验证了文中方法能够利用有限的数据库合成演员在用户指定运动和视角下的真实感视频.     

Two-dimensional entropy model for video shot partitioning

A shot presents a contiguous action recorded by an uninterrupted camera operation and frames within a shot keep spatio-temporal coherence. Segmenting a serial video stream file into meaningful shots is the first pass for the task of video analysis,content-based video understanding. In this paper,a novel scheme based on improved two-dimensional entropy is proposed to complete the partition of video shots. Firstly,shot transition candidates are detected using a two-pass algorithm: a coarse searching pass and a fine searching pass. Secondly,with the character of two-dimensional entropy of the image,correctly detected transition candidates are further classified into different transition types whereas those falsely detected shot breaks are distinguished and removed. Finally,the boundary of gradual transition can be precisely located by merging the characters of two-dimensional entropy of the image into the gradual transition. A large number of video sequences are used to test our system performance and promising results are obtained.     

基于模式模板的MPEG压缩视频序列的划变转换检测

为了在进行视频分段时，对一些复杂的和奇异的Wipe转换模式进行检测，得出了一种基于模式模板的MPEG压缩视频序列的划变转换检测方法。该方法是在刻画划变转换时，用一种视频帧间图象特征变化信息的模式模板；并用一个基于该模式模板的算法，来检测包括各种奇异转换在内的划变转换，同时为了消除运动干扰，还引入了Hough变换。实验结果显示，该方法能精确检测划变转换区的位置和识别相应的转换模式，另外，无论是已存在的或将来出现的划变转换模式，该算法都同样适用。     

Automatic partitioning of full-motion video

Partitioning a video source into meaningful segments is an important step for video indexing. We present a comprehensive study of a partitioning system that detects segment boundaries. The system is based on a set of difference metrics and it measures the content changes between video frames. A twin-comparison approach has been developed to solve the problem of detecting transitions implemented by special effects. To eliminate the false interpretation of camera movements as transitions, a motion analysis algorithm is applied to determine whether an actual transition has occurred. A technique for determining the threshold for a difference metric and a multi-pass approach to improve the computation speed and accuracy have also been developed.