Scene change detection

In this paper, we derive a principal component regression (PCR) method for estimating the optical flow between frames of video sequences according to a pel-recursive manner. This is an easy alternative to dealing with mixtures of motion vectors due to the lack of too much prior information on their statistics (although they are supposed to be normal). The 2D motion vector estimation takes into consideration local image properties. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Preliminary experiments indicate that this approach provides robust estimates of the optical flow. KEY WORDS Motion estimation, principal component regression, and surveillance. 1.

A new data hiding scheme for scene change detection in H.264 streams is presented. The proposed scheme takes advantage of the different block sizes used by the H.264 encoder during the inter prediction stage in order to hide the scene change information inside the encoded sequence. This information enables real time scene change detection directly from the H.264 stream.

Pel-recursive motion estimation is a well-established approach. However, in the presence of noise, it becomes an ill-posed problem that requires regularization. In this paper, motion vectors are estimated in an iterative fashion by means of the Expectation-Maximization (EM) algorithm and a Gaussian data model. Our proposed algorithm also utilizes the local image properties of the scene to improve the motion vector estimates following a spatially adaptive approach. Numerical experiments are presented that demonstrate the merits of our methods.

The paper focuses on an approach to a near-duplicate videos search. The search is based on the comparison of scene relative lengths in the space L2. The comparison is made with Gale-Church hypothesis. The concept "shot descriptor" was introduced. To speed up the performance of this method, semantic hashing was suggested, i.e. a generalization of locally sensitive hashing. 

В работе рассмотрен подход для поиска нечетких дубликатов видео. Поиск основан на сравнении относительных длин сцен в пространстве L2. Сравнение проводится с учетом гипотезы Гейла-Черча. Вводится понятие «дескриптора сцены». Для ускорения работы метода предложено использовать семантическое хеширование, обобщение локально чувствительного хеширования.

Principal Component Analysis -Engineering Applications 102 and other applications. On the other hand, the main drawback of the standard KPCA is that the huge amount of computation required, and the space needed to store the kernel matrix. KPCA can be viewed as a primal space problem with samples created via incomplete Cholesky decomposition. Therefore, all the efficient PCA algorithms can be easily adapted into KPCA. Furthermore, KPCA can be extended to a mixture of local KPCA models by applying the mixture model to probabilistic PCA in the primal space. The theoretical analysis and experiments can shed light on the performance of the proposed methods in terms of computational efficiency and storage space, as well as recognition rate, especially when the number of data points n is large.

The paper focuses on the algorithms of the event detection in content-based video retrieval. Video has a complex structure and can express the same idea in different ways. This makes the task of searching for video more complicated. Video titles and text descriptions cannot give the whole information about objects and events in the video. This creates a need for content-based video retrieval. There is a semantic gap between low-level video features, that can be extracted, and the users’ perception. The task of event detection is reduced to the task of video segmentation. Complex content-based video retrieval can be regarded as the bridge between traditional retrieval and semantic-based video retrieval. The properties of video as a time series are described. Introduced the concept of anomalies in the video. A method for event detection based on comparing moving averages with windows of different sizes is proposed. According to the classification given at the beginning of this article, our method refers to statistical methods. It differs from other methods with low computational complexity and simplicity. The video stream processing language is proposed for function-based description of video handling algorithms. So, our method is formulated in the form of a declarative description on an interpreted programming language. Unfortunately, most of the existing video processing methods use exclusively imperative approach, which often makes understanding more difficult. Examples of the use of this language are given. Its grammar is described too. As shown by experiments, the implementation of the proposed video events retrieval method, unlike their counterparts, can work for video streams too with a real-time and potentially infinite frame sequences. Such advantages within low computational requirements make implementation of the method helpful in aviation and space technology. The algorithm has some disadvantages due to necessity parameter selection for particular task classes. The theorem on near-duplicates of video is formulated at the end of the article. It asserts the near-duplicate videos express the same sequence of phenomena.

In this paper, we derive a principal component regression (PCR) method for estimating the optical flow between frames of video sequences according to a pel-recursive manner. This is an easy alternative to dealing with mixtures of motion vectors due to the lack of too much prior information on their statistics (although they are supposed to be normal). The 2D motion vector estimation takes into consideration local image properties. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.

Automated surveillance systems have drawn much attention recently. For a surveillance system to operate and obtain credible sensing of a scenario, visual analysis is the most critical aspect. With an increasing expectation of acceptable outcomes from surveillance systems, light weight but fast visual analyses for producing authentic results are being greatly emphasized. In this paper, we present a new approach for detecting fast scene change in the sequentially captured photographs of a focused location. The system considers the images in RGB color format and analyzes all 3 color layers. In this method, the color value histograms of the images were compared to each other using a statistical procedure called ANOVA. We devised an approach that approximately identifies the locality of change in the scene. Various experimental outcomes based on this visual computation process have also been provided in this paper.

Даны два видео файла или потока. Нужно выяснить являются ли они дубликатами друг друга. Здесь, под словом дубликат понимается не формализуемое условие: «На этих файлах изображено одно и то же?».  Возможна, и другая постановка этой задачи. Дан набор видео, для нового добавляемого в этот набор видео нужно определить,  есть ли дубликат этого нового видео в наборе или нет. Понятие «нечеткий дубликат» является устоявшимся термином в задачах информационного поиска и обычно употребляется применительно к документам и изображениям. Нечеткость в данном случае интерпретируется как неполное или частичное совпадение документов или изображений.

Digital audio & video data have become an integral part of multimedia information systems. To reduce storage and bandwidth requirements, they are commonly stored in a compressed format, such as MPEG-1. Increasing amounts of MPEG encoded audio and video documents are available online and in proprietary collections. In order to effectively utilise them, we need tools and techniques to automatically analyse, segment, and classify MPEG video content. Several techniques have been developed both in the audio and visual domain to analyse videos. This paper presents a survey of audio and visual analysis techniques on MPEG-1 encoded media that are useful in supporting a variety of video applications. Although audio and visual feature analyses have been carried out extensively, they become useful to applications only when they convey a semantic meaning of the video content. Therefore, we also present a survey of works that provide semantic analysis on MPEG-1 encoded videos.

Никитин И. К. Элементы ассоциативного поиска по видео [Текст] / И. К. Никитин // Новое слово в науке: перспективы развития : материалы междунар. науч.–практ. конф. (Чебоксары, 10 сент. 2014 г.) / редкол.: О. Н. Широков [и др.]. – Чебоксары: ЦНС «Интерактив плюс», 2014. – С. 223–230. – ISBN 978-5-906626-37-0.

За последние несколько лет объем носителей мультимедийной информации вырос в несколько раз. В сложившейся ситуации необходим простой и гибкий поиск неструктурированным мультимедийных данных. В статье предлагается обзор  существующих методов ассоциативного поиска по видео.

A novel scene change detection algorithm is proposed in this paper that exploits both audio and video information. Audio frames are projected to the eigenspace and their distance from a reference noise eigenframe is calculated. An analysis is presented that explains why this subspace favors scene cut detection. Video information is used to align audio scene change indications with neighboring shot changes in the visual data, and accordingly to reduce the false alarm rate. Moreover, video fade effects are identified and used independently in order to track scene changes. The detection technique was tested on newscast videos provided by the TRECVID 2003 video test set [1]. The experimental results show that the aforementioned methods used to process the audio and video information complement each other well in tackling the scene change detection problem.

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. In this paper, a novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on 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.

Digital audio & video data have become an integral part of multimedia information systems. To reduce storage and bandwidth requirements, they are commonly stored in a compressed format, such as MPEG-1. Increasing amounts of MPEG encoded audio and video documents are available online and in proprietary collections. In order to effectively utilise them, we need tools and techniques to automatically analyse, segment, and classify MPEG video content. Several techniques have been developed both in the audio and visual domain to analyse videos. This paper presents a survey of audio and visual analysis techniques on MPEG-1 encoded media that are useful in supporting a variety of video applications. Although audio and visual feature analyses have been carried out extensively, they become useful to applications only when they convey a semantic meaning of the video content. Therefore, we also present a survey of works that provide semantic analysis on MPEG-1 encoded videos.

With the enormous growth in digital audiovisual (AV) information in our life, there is an important need for tools which enable describing the AV content information. In this context, the MPEG-7 standard was developed in order to provide a set of standardized description tools which generate metadata about AV content. However, before any content-based manipulations, the hierarchical structure of video must be determined. This process is known as shot boundary detection or in other case scene change detection. In this paper, an old and reliable method based on local histogram has been used to implement shot cut detector for real-time applications. Since software implementation on PC is not suitable for this algorithm due to the sequential treatments of the processor, we have used an FPGA-based platform. (L. Boussaid), Abdellatif.mtibaa@enim.rnu.tn (A. Mtibaa), mohamed.abid@enis.rnu.tn (M. Abid), paindav@u-bourgone.fr (M. Paindavoine). 0920-5489/$ -see front matter

Digital audio & video data have become an integral part of multimedia information systems. To reduce storage and bandwidth requirements, they are commonly stored in a compressed format, such as MPEG-1. Increasing amounts of MPEG encoded audio and video documents are available online and in proprietary collections. In order to effectively utilise them, we need tools and techniques to automatically analyse, segment, and classify MPEG video content. Several techniques have been developed both in the audio and visual domain to analyse videos. This paper presents a survey of audio and visual analysis techniques on MPEG-1 encoded media that are useful in supporting a variety of video applications. Although audio and visual feature analyses have been carried out extensively, they become useful to applications only when they convey a semantic meaning of the video content. Therefore, we also present a survey of works that provide semantic analysis on MPEG-1 encoded videos.

Digital audio & video data have become an integral part of multimedia information systems. To reduce storage and bandwidth requirements, they are commonly stored in a compressed format, such as MPEG-1. Increasing amounts of MPEG encoded audio and video documents are available online and in proprietary collections. In order to effectively utilise them, we need tools and techniques to automatically analyse, segment, and classify MPEG video content. Several techniques have been developed both in the audio and visual domain to analyse videos. This paper presents a survey of audio and visual analysis techniques on MPEG-1 encoded media that are useful in supporting a variety of video applications. Although audio and visual feature analyses have been carried out extensively, they become useful to applications only when they convey a semantic meaning of the video content. Therefore, we also present a survey of works that provide semantic analysis on MPEG-1 encoded videos.

Several scene-change detection algorithms have been proposed in literature up to now. Most of them use fixed thresholds for the similarity metrics used to decide if there was a change or not. These thresholds are obtained by empirically or they must be calculated before the detection after the whole sequence is obtained. Performance of most scene change detectors decreases considerably for videos with high scene complexity and variation. In this paper, we review previous works in this area, and study the correlation between local statistical characteristics, scene duration and scene change. Based on this analysis, we further propose and implement a novel scene change detection method for H.264 codec, defining an automated, dynamic threshold model which can efficiently trace scene changes. Experimental results on QCIF videos indicate very good performance with significantly improved accuracy combined with minimum complexity.

The widespread adoption of the JPEG2000 standard calls for the development of computationally efficient algorithms to analyze the content of imagery compressed using this standard. For this purpose, we propose the use of the information content (IC) of wavelet subbands, defined as the number of bytes that JPEG2000 spends to encode the subbands. The IC of subbands can be obtained from the packet headers of the JPEG2000 codestream, thereby avoiding decompressing the arithmetically encoded bitplane data. We p resent experimental results for two content analysis tasks; namely, image classification and scene change detection. Our results indicate that performance comparable to that of methods operating on decompressed data can be achieved, while saving computational and bandwidth resources.

Abstract. In this paper, we derive a principal component regression (PCR) method for estimating the optical flow between frames of video sequences according to a pel-recursive manner. This is an easy alternative to dealing with mixtures of motion vectors due to the lack of too much prior information on their statistics (although they are supposed to be normal). The 2D motion vector estimation takes into consideration local image properties. The main advantage of the developed procedure is that no knowledge of the noise distribution is ...

Daily increase in the number of available video material resulted in significant research efforts for development of advanced content management systems. First step towards the semantic based video indexing and retrieval is a detection of elementary video structures. In this paper we present the algorithm for finding shot boundaries by using spectral clustering methods. Assuming that a shot boundary is a global feature of the shot rather then local, this paper introduces the algorithm for scene change detection based on the information from eigenvectors of a similarity matrix. Instead of utilising similarities from consecutive frames, we treat each shot as a cluster of frames. Objective function which is used as the criteria for spectral partitioning sums contributions of every frame to the overall structure of the shot. It is shown in this paper that optimizing this objective function gives proper information about scene change in the video sequence. Experiments showed that obtained scenes can be merged to from clusters with similar content, suitable for video summarisation. Evaluation is done on different datasets, and results are presented and discussed.

U radu je predložen postupak multisenzorskog izdvajanja i ažuriranja slike pozadine koji se koristi za video nadzor i detekciju pokretnih objekata. Izdvajanje slike pozadine zasnovano je na detekciji promena na slici i iterativnom postupku ažuriranja. Pri detekciji promena koriste se informacije raspoloživih senzora, čime se povećava pouzdanost detekcije i slike pozadine. Predloženi postupak testiran je na bazama registrovanih televizijskih i termovizijskih slika. Ključne reči: multisenzorska detekcija, izdvajanje slike pozadine, inicijalizacija i ažuriranje slike pozadine.

In produced video such as television or movies, shots are separated by different types of transitions, or boundaries. Although well known video editing programs such as Adobe Premiere or Ulead Media Studio provide more than 100 different types of edits, we classify in ...

Существует достаточно широкий круг задач, где требуется анализ, аудио-визуальных моделей реальности. Для многих военных и гражданских приложений, необходимо наличие возможности поиска нечетких дубликатов видео. К таким приложениям можно отнести формирование поисковых сниппетов для видео-материала; поиск «пиратского» содержимого; оптическую навигацию БПЛА. Понятие «нечеткий дубликат» означает неполное или частичное совпадение текущего документа (изображения) с другим документом подобного класса. Существует несколько вариантов поиска нечетких дубликатов, но большинство подходов требуют значительных затрат либо по времени, либо по памяти. Многие методы рассматривают только содержимое кадров и не учитывают временные особенности видео. В этой работе используются сравнение относительных длин сцен для обнаружения нечетких дубликатов. Подобный подход применяется в математической лингвистике для выравнивания параллельных корпусов текстов на разных языках. Автор считает, что два нечетких дубликата видео являются «переводами» друг друга. Требуется установить близость таких «переводов». Относительная длина сцены вычисляется как вектор отношений абсолютной длины сцены к абсолютным длинам остальных сцен видео. Относительные длины сцен двух нечетких дубликатов редко будут совпадать. Это связано, в том числе, с ошибками распознавания границ сцен. Если относительная длина сцены одного видео отличается от длины сцены другого видео не более чем в два раза, и все предыдущие сцены выравнены, то, текущая пара сцен выражает одно и то же явление, при условии, что оба видео являются нечеткими дубликатами друг друга. Чем менее отличаются относительные длины сцен, тем более вероятно, что сцены похожи. Если длины отличаются больше чем в два раза, то длину меньшей сцены складывают с длиной следующей сцены этого же видео, и рассматривают объединенную сцену как одну. В случае совпадения относительных длин сцен видео применяется сравнение начальных и конечных кадров внутри сцен. Для решения задачи используются методы опорных векторов и мешка слов.

В работе рассмотрен подход для поиска нечетких дубликатов видео. Поиск основан на сравнении относительных длин сцен в пространстве L2. Сравнение проводится с учетом гипотезы Гейла-Черча. Вводится понятие «дескриптора сцены». Для ускорения работы метода предложено использовать семантическое хеширование, обобщение локально чувствительного хеширования.

Существует широкий круг задач, где требуется анализ, аудио-визуальных моделей реальности. В частности, для многих военных и гражданских приложений, необходимо наличие поиска нечетких дубликатов видео. Для мирного применения, — это группировка подсказок в выдаче поисковиков, поиск неправомерных публикаций видеофайлов, сокращение объема хранимой и передаваемой информации. Для военного применения можно тоже перечислить несколько вариантов.

This work presents a new approach to detecting the scene change in the successive capture of photographs of a place within equal time interval. This method is based on a gray level histogram of every image. In this method the histogram of an image is processed to modify it for ...

This work presents a new video feature extraction technique based on the Generalized Hough Transform (GHT). This technique provides a way to define a similarity measure between two different frames, which establishes the basis for scene cut detection algorithms. Moreover, GHT allows to calculate the differences between two frames in terms of rotation, scale and displacement. This provides a framework for the development of global motion estimation algorithms. In addition, gradual transition detection algorithms (fades, dissolves, etc.) can also be developed.

There are situations, where video surveillance based on automated scene change detection systems may prove to be ineffective. In some cases, the changes in the image are too small to be properly detected. In others, false alarms are generated without objects (or people) moving. These alarms may be caused by changes in illumination, e.g., a faulty lamp or spurious reflections in places near water pools. Further, the absence of illumination during a blackout (whether it is caused by accident or on purpose) prevents cameras from their surveillance operation. There are high security installations for which it is necessary to introduce reliable, independent and effective sensors that can keep the surveillance work even during a blackout. Laser range scanners are electronic instruments measuring the distance from the instrument itself to the outside world along a specific direction. The type of the instrument to use depends on the range of distances to measure. The deflection of the laser beam (e.g., using a rotating mirror) enables the acquisition of the distance profiles of the surrounding premises in a very short time. Alarms are generated by comparing acquired distance measurements (organized as profiles -2D Laser Surveillance, or in clouds of points -3D Laser Surveillance) with previously acquired references. This is most effective for real-time detection of even small changes in the environment. The main feature of laser surveillance system is that it measures distances between the instrument itself and the real world, rather than measuring only appearance as video surveillance systems do. As such, any physical change occurring within the field of site of the laser instrument is immediately measured and detected. The fact that laser systems are self-illuminating and do not depend on the external illumination make them quite effective to work during blackouts. The paper introduces laser surveillance and shows how such systems complement well already existing video surveillance systems.

With the enormous growth in digital audiovisual (AV) information in our life, there is an important need for tools which enable describing the AV content information. In this context, the MPEG-7 standard was developed in order to provide a set of standardized description tools which generate metadata about AV content. However, before any content-based manipulations, the hierarchical structure of video must be determined. This process is known as shot boundary detection or in other case scene change detection. In this paper, an old and reliable method based on local histogram has been used to implement shot cut detector for real-time applications. Since software implementation on PC is not suitable for this algorithm due to the sequential treatments of the processor, we have used an FPGA-based platform. (L. Boussaid), Abdellatif.mtibaa@enim.rnu.tn (A. Mtibaa), mohamed.abid@enis.rnu.tn (M. Abid), paindav@u-bourgone.fr (M. Paindavoine). 0920-5489/$ -see front matter

A motion picture can be modeled as a composition of many scenes where each scene is comprised of multiple shots. Thus, a conventional movie is a sequential aggregation of a large number of disparate image sequences. Within each image sequence or shot, there is consistency in image content and dynamics. This consistency in dynamics can be used in identifying scene changes for video segment decomposition and for techniques to improve data compression.

Besides being an ill-posed problem, the pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalized Cross Validation (GCV) to estimate the best regularization scheme for a given moving pixel. In our model, a regularization matrix carries information about different sources of error in its entries and motion vector estimation takes into consideration local image properties following a spatially adaptive. Preliminary experiments
indicate that this approach provides robust estimates of the optical flow

The novel H.264/AVC video coding standard introduces many improvements in inter-frame motion.-compensated prediction, although significantly increasing the computational complexity of the motion estimation process. In this paper, we propose a fast-block based predictive-recursive motion estimation algorithm, which applies two consecutive search steps, achieving same image quality of the classical Full-Search Block Matching by using only 3% of the computation. Furthermore, the output of the first search step can be used for efficient-on-the fly detection of scene changes in the video sequence, and interlaced or progressive coding decision for each picture.