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About 10,000 papers for “Video content analysis”

Video Content Analysis

International Journal of Advance Engineering and Research Development · 2014

Cited by 0Open access

Video Content Analysis

Bronec, Jakub · Open Repository and Bibliography (University of Luxembourg) · 2019

Cited by 0

Video Content Analysis

Maksym Havrylov, P.U. Ogeenko, В.В. Губкина · Institutional Repository National Mining University of Ukraine (National Mining University of Ukraine) · 2017

Video content analysis (VCA) is the capability of automatically analyzing video to detect and determine temporal events not based on a single image and can be considered as the automated equivalent of the biological visual cortex.This technical capability is used in a wide range of domains including entertainment, health-care, retail, automotive, transport, home automation, safety and security.The algorithms can be implemented as software on general purpose machines, or as hardware in specialized video processing units.A lot of different functionalities can be implemented in VCA.Video Motion Detection is one of the simplest forms where motion is detected with regard to a fixed background scene.More advanced functionalities include video tracking (used to determine the location of persons or objects in the video signal, possibly with regard to an external reference grid) and egomotion estimation (used to determine the location of a camera by analyzing its output signal).Based on the internal representation that VCA is generated in the machine, it is possible to build other functionalities, such as identification, behavior analysis or other forms of situation awareness.VCA relies on good input video, so it is often combined with such video enhancement technologies as video denoising, image stabilization, unsharp masking and super-resolution.Speaking about it functionalities such features can be added as dynamic masking ( blocking a part of the video signal based on the signal itself, for example because of privacy concerns),object detection ( used to determine the presence of a type of object or entity, for example a person or car.Other examples include fire and smoke detection), recognition (face recognition and Automatic Number Plate Recognition are used to recognize, and therefore possibly identify persons or cars).Such functionalities as motion detection and people counting are believed to be available as commercial off-the-shelf products with a decent trackrecord, even such freeware as Flowstone can handle movement and color analysis.In many domains VCA is implemented on CCTV systems and is either distributed on the cameras (at-the-edge) or centralized on dedicated processing systems.Video Analytics and Smart CCTV are commercial terms for VCA in the security domain.In the UK the BSIA has developed an introduction guide for VCA in the security domain.Audio analytics can be also used in addition to video analytics and to complement it.Kinect is an add-on peripheral for the Xbox 360 gaming console that uses VCA as a part of the user input.VCA is a new technology but it is highly promising one.New applications are frequently found, however the track record of different types of VCA differs widely.

Cited by 0Open access

Content Analysis of Compressed Video

W. Brent Seales, Cheng J. Yuan, Wei Hu et al. · 1996

Continuous video applications such as video transfer, searching and display can save time and space by operating on compressed data. The problem is that most mid and high level image operations, such as object recognition, are formulated as sequences of operations in the image domain. Such methods need direct access to pixel information as a starting point, but the pixel information in a compressed image stream is not immediately accessible. In this paper we show how to perform object recognition directly on compressed images or continuous video streams without recovering explicit pixel information. Our approach is based on eigenvectors constructed from compressed image data. Distance in eigenspace, which is the basis for classification, is preserved under linear orthogonal transformations. The Discrete Cosine Transform therefore preserves distance in eigenspace, allowing for exactly the same classification results on transformed images. We show that the classification measure is robu...

Cited by 0

Real-time video content analysis

Viktor S. Wold Eide, Ole‐Christoffer Granmo, Frank Eliassen et al. · ACM Transactions on Multimedia Computing Communications and Applications · 2006

Real-Time content-based access to live video data requires content analysis applications that are able to process video streams in real-time and with an acceptable error rate. Statements such as this express quality of service (QoS) requirements. In general, control of the QoS provided can be achieved by sacrificing application quality in one QoS dimension for better quality in another, or by controlling the allocation of processing resources to the application. However, controlling QoS in video content analysis is particularly difficult, not only because main QoS dimensions like accuracy are nonadditive, but also because both the communication- and the processing-resource requirements are challenging.This article presents techniques for QoS-aware composition of applications for real-time video content analysis, based on dynamic Bayesian networks. The aim of QoS-aware composition is to determine application deployment configurations which satisfy a given set of QoS requirements. Our approach consists of: (1) an algorithm for QoS-aware selection of configurations of feature extractor and classification algorithms which balances requirements for timeliness and accuracy against available processing resources, (2) a distributed content-based publish/subscribe system which provides application scalability at multiple logical levels of distribution, and (3) scalable solutions for video streaming, filtering/transformation, feature extraction, and classification.We evaluate our approach based on experiments with an implementation of a real-time motion vector based object-tracking application. The evaluation shows that the application largely behaves as expected when resource availability and selections of configurations of feature extractor and classification algorithms vary. The evaluation also shows that increasing QoS requirements can be met by allocating additional CPUs for parallel processing, with only minor overhead.

Cited by 4

Content Analysis of Video Sequences

M.T. Orchard, Robert A. Joyce · 2002

Abstract : Content analysis of video sequences is a critical component of virtually any application involving the management of large video data sets. Systems for managing video databases or transmitting large numbers of video sequences are most efficient if they incorporate some understanding of the video content. The required analysis must be automated if large volumes of video are to be accommodated. This report presents two contributions of this project to problems in video content analysis. The first is an algorithm for fast estimation of scene boundaries in video sequences. The second is an approach for using video content and short-term traffic statistics to improve bandwidth allocation when transmitting video over the internet.

Cited by 0

Personalized video adaptation based on video content analysis

Min Xu, Jesse S. Jin, Suhuai Luo · 2008

Personalized video adaptation is expected to satisfy individual users' needs on video content. Multimedia data mining plays a significant role of video annotation to meet users' preference on video content. In this paper, a comprehensive solution for personalized video adaptation is proposed based on video content mining. Video content mining targets both cognitive content and affective content. Cognitive content refers to those semantic events, which are very specific for the video domains. Sometimes, users might prefer "emotional decision" to select their interested video content. Therefore, we introduce affective content which causes audiences' strong reactions.

Cited by 13

Content analysis of user generated video

Golnaz Abdollahian · Purdue e-Pubs (Purdue University System) · 2009

Due to the availability of online repositories, such as YouTube and social networking sites, there has been a tremendous increase in the amount of personal video generated and consumed by average users. Such video is often referred to as user generated video (UGV) or consumer video as opposed to produced video, which is produced and edited by professionals. Examples of produced video are television programs, movies, and commercials. The large amount of user generated content available has increased the need for compact representations of UGV sequences that are intuitive for users and let them easily and quickly browse large collections of video data, especially on portable devices, such as mobile phones. UGV has special properties that are distinct from the properties of produced video, which makes the content analysis for this type of video more challenging. Many analysis techniques that are designed for produced video can not be directly used for UGV. In this thesis we developed a system for the analysis of UGV. The system consists of two major components: camera motion-based video summarization and video annotation based on location information. UGV often has a rich camera motion structure that is generated at the time the video is recorded by the person taking the video, i.e. the “camera person.” We exploit this structure by defining a new concept known as camera view for temporal segmentation of UGV. The segmentation provides a video summary with unique properties that is useful in applications such as video annotation. Camera motion is also a powerful feature for identification of keyframes and regions of interest (ROIs) since it is an indicator of the camera person’s interests in the scene and can also attract the viewers’ attention. We examined the effect of camera motion on human visual attention through an eye tracking experiment. The results showed a high dependency between the distribution of fixation points of the viewers and the direction of camera movement. Based on the results of this experiment, we introduced a new location-based saliency map which is generated based on camera motion parameters. This map is combined with other saliency maps generated using features such as color contrast, object motion and face detection to determine the ROIs. The output of the summarization step is a set of keyframes with highlighted ROIs. Several user studies were designed and conducted to assess the performance of our system. The subjective evaluation indicated that our system produces video summaries that are consistent with viewers’ preferences. The video annotation part in our proposed system generates a set of tags or keywords for the video which is provided to the user along with the video summary. Our proposed annotation approach uses the location metadata of the video and visual features to find tags that are most likely relevant to the video in a user-tagged image database. We examined different image matching methods to be used to measure the visual similarity between the video keyframes and images in the database. These methods have been tested and compared to each other over a variety of different cases.

Cited by 0

Content-Based Analysis of Digital Video

Alan Hanjalić · Kluwer Academic Publishers eBooks · 2004

Content-Based Analysis Of Digital Video focuses on fundamental issues underlying the development of content access mechanisms for digital video. It treats topics that are critical to successfully automating the video content extraction and retrieval processes, and includes coverage of: - Video parsing, - Video content indexing and representation, - Affective video content analysis. In this well illustrated book the author integrates related information currently scattered throughout the literature and combines it with new ideas into a unified theoretical approach to video content analysis. The material also suggests ideas for future research. Systems developers, researchers and students working in the area of content-based analysis and retrieval of video and multimedia in general will find this book invaluable

Cited by 70Open access

Automatic Video Content Analysis and Management

Yong Rao · 2008

The principal goal of this proposal is to design an automatic video processing architecture that will enable us to design and develop efficient multimedia applications such as summarization, indexing, retrieval and others. While there are many research projects in this area, many, if not all, rely on MPEG-7 for high-level semantic retrieval. The proposed architecture is compatible with MPEG-7 in that the proposed architecture can be annotated using the MPEG-7 standard, but we emphasize on user relevance feedback in the high-level semantic retrieval task. The proposed research is built upon a substantial body of preliminary work that serves as proof of concept for the proposed architecture and other techniques. The preliminary work includes the development of automatic clustering technique using Delaunay Triangulation, automatic video summary generation and automatic shot boundary detection. We address the problem of automatic video indexing and organization and video information retrieval through user interaction at multiple levels. Our work focuses on the following three important areas: (1) temporal video segmentation; (2) construction of

Cited by 0

Interactive learning for video content analysis

Ewine Smits · Research Repository (Delft University of Technology) · 2008

We propose a method for identifying segments of a video that represent the events preferred by the user. Possible applications are personalized browsing through music DVDs or smart surveillance systems that can adapt to new circumstances. Requirements for this system are that it is generic and adaptable to the user and to new circumstances. Interactive learning techniques meet these requirements. Interactive learning methods are machine learning methods that involve the user in the learning process in some way. Interactive learning methods do not need information about the problem beforehand and make a system adaptable. For these reasons we propose to apply interactive learning techniques to video content analysis. We chose to implement a user interface that allows users to indicate the relevance of parts of a video by adapting a curve. This curve is used for labelling the underlying audio feature vectors to use as a training set for a classifier. The scenario in which a user adapts a relevance curve while watching a video turned out to be a good way of interacting with the user. The scenario implicates some restrictions for the system, that have turned out to be very strict for the classifier. This classifier needs to be capable of learning efficiently from different sized training sets, handling unbalanced datasets, handling problems with different complexities and not take too much training and classification time. The key findings with respect to our music DVD test scenario are: • Choices about the best classifier and its parameters without any information about events or data can not be optimized because of the lack of prior information. • Support Vector (SV) classifiers need extensive parameter optimization in order to get good results; no optimization means bad results in general and optimizing based on the (small) training set is very time-consuming and can cause overfitting. Because of the constraints in the SV algorithm, this classifier might not handle highly overlapping classes well. These reasons make a SV classifier not a suitable choice for our application. • A Nearest Neighbour (NN) classifier, adapted in such a way that the prior probabilities are taken into account, gave the best overall results on our data. This can be explained by the fact that this classifier has no heavy parameter tuning to perform. • Unbalanced datasets do not have to pose a problem as classifiers can be adapted and post processed to use this information. This same adaptation strategy can be applied for classes that have different classification costs. • User feedback can be used in an 'active learning' manner: Hard to classify samples are labelled and these can be stressed in order to focus more on these samples for better results. To adapt this to surveillance videos the following steps need to be taken: • A general video feature set needs to be chosen and, if necessary, combined with the audio feature set. • Sub sampling needs to be implemented so the system can 'forget' old samples and this way adapt to new circumstances. • The first training is best performed on an offline database or by attaching it to an existing system. This will reduce the training time that is expected to be high because at least a few examples of threat situations need to be labelled, while these situations are rare. Interactive learning is a promising concept to be applied to video concept detection. This thesis analyses the possibilities and restrictions of applying interactive learning to our scenario.

Cited by 0Open access

Video texture analysis for VVC content

Fatma Belghith, Damak Taheni, Ben Jdidia Sonda et al. · 2024

Examining video characteristics, particularly leveraging filters such as Canny and calculating image standard deviation, prior to the video coding process is a crucial preprocessing step that enhances the efficiency and quality of the coding workflow. By applying filters like Canny, valuable insights into the spatial distribution of edges are gained, enabling precise identification of regions with varying complexity. Simultaneously, the calculation of standard deviation provides a quantitative measure of pixel intensity variations, aiding in the assessment of overall image texture. This meticulous analysis, as proposed by our method, enables the optimization of coding parameters, including the selection of suitable compression algorithms and the refinement of spatial and temporal redundancy reduction strategies. Ultimately, the strategic incorporation of filter-based analyses in the preprocessing phase not only refines video coding processes but also lays the foundation for improved compression outcomes, addressing the unique characteristics of each video sequence with precision.

Cited by 0

Content-based analysis of news video

Junqing Yu, Dongru Zhou, Huayong Liu et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001

In this paper, we present a schema for content-based analysis of broadcast news video. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.

Cited by 0