Shot Boundary Detection Research Articles

Tsallis entropy-based information measures for shot boundary detection and keyframe selection

Automatic shot boundary detection and keyframe selection constitute major goals in video processing. We propose two different information-theoretic approaches to detect the abrupt shot boundaries of a video sequence. These approaches are, respectively, based on two information measures, Tsallis mutual information and Jensen–Tsallis divergence, that are used to quantify the similarity between two frames. Both measures are also used to find out the most representative keyframe of each shot. The representativeness of a frame is basically given by its average similarity with respect to the other frames of the shot. Several experiments analyze the behavior of the proposed measures for different color spaces (RGB, HSV, and Lab), regular binnings, and entropic indices. In particular, the Tsallis mutual information for the HSV and Lab color spaces with only 8 regular bins for each color component and an entropic index between 1.5 and 1.8 substantially improve the performance of previously proposed methods based on mutual information and Jensen–Shannon divergence.

Video Retrieval: An Adaptive Novel Feature Based Approach for Movies

Video Retrieval is a field, where many techniques and methods have been proposed and have claimed to perform reliably on the videos like broadcasting of news & sports events. As a movie contains a large amount of visual information varying in random manner, it requires a highly robust algorithm for automatic shot boundary detection as well as retrieval. In this paper, we described a new adaptive approach for shot boundary detection which is able to detect not only abrupt transitions like hard cuts but also special effects like wipes, fades, and dissolves as well in different movies. To partition a movie video into shots and retrieve many metrics were constructed to measure the similarity among video frames based on all the available video features. However, too many features will reduce the efficiency of the shot boundary detection. Therefore, it is necessary to perform feature reduction for every decision. For this purpose we are following a minimum features based algorithm.

Shot Boundary Detection Method for News Video

It is very important to detect shot boundary accurately and quickly in a large number of news video data. Therefore, we proposed a new method with dual-detection model. The method is divided into two stages, i.e. pre-detection and re-detection. In the pre-detection stage, the uneven blocked differences based on the feature of human vision are presented and used in adaptive binary search to detect shot boundaries. In the re-detection round, Speeded Up Robust Features (SURF) method is applied to exclude false detections. The experimental results show that this method can detect abrupt boundaries of news video quickly and accurately.

Medical Video Summarization using Central Tendency-Based Shot Boundary Detection

Due to the advancement in multimedia technologies and wide spread usage of internet facilities; there is rapid increase in availability of video data. More specifically, enormous collections of Medical videos are available which has its applications in various aspects like medical imaging, medical diagnostics, training the medical professionals, medical research and education. Due to abundant availability of information in the form of videos, it needs an efficient and automatic technique to manage, analyse, index, access and retrieve the information from the repository. The aim of this paper is to extract good visual content representatives – Summary of keyframes. In order to achieve this, the authors propose a new method for video shot segmentation which in turn leads to extraction of better keyframes as representative for summary. The proposed method is experimented and evaluated using publically available medical videos. As a result, better precision and recall is obtained for shot detection when compared to that of the recent related methods. Evaluation of video summary is done using fidelity measure and compression ratio.

Various Fast Block Matching Algorithm for Video Shot Boundary Detection

Various methods have been proposed and developed for video shot boundary detection. Commonly used method for shot detection is Block Matching Algorithm. It is the most commonly used motion estimation technique. Various block matching algorithms have been proposed for shot detection such as Exhaustive Search, Three Step Search, New Three Step Search, Simple and Efficient Search, Four Step Search, Diamond Search, Adaptive Rood Pattern Search. This paper presents the comparative study of various Block Matching Algorithm used for boundary detection in videos.

A Novel Approach for Tracking with Implicit Video Shot Detection

Video shot detection - Shot change detection is an essential step in video content analysis. The field of Video Shot Detection (VSD) is a well exploited area. In the past, there have been numerous approaches designed to successfully detect shot boundaries for temporal segmentation. Robust Pixel Based Method is used to detect shot changes in a video sequence. Tracking algorithm is a time consuming process due to the large amount of data contained in video using the video shot detection the computational cost can be reduced to a great extent by the discarding the frames which are not of any interest for the tracking algorithm. In this paper we present a novel approach of combining the concepts of Video shot detection and Object tracking using particle filter to give us a efficient Tracking algorithm with implicit shot detection. Keywords - Bhattacharyya distance, Local adaptive threshold, Particle filter, Robust Pixel Difference method, Residual Re-sampling, Shot detection. I. INTRODUCTION The rapid development of storage and multimedia technologies has made the retrieval and processing of videos relatively easy. Temporal segmentation is a fundamental step in video processing, and shot change detection is the most basic way to achieve it. However, while hard cuts (abrupt transitions) can be easily detected by finding changes in a color histogram, gradual transitions such as dissolves, fades, and wipes are hard to locate. In practice, however, 99% of all edits fall into one of the following four categories hard cuts, fades, wipes and dissolves. Many shot change detection studies focus on finding low-level visual features, e.g., color histograms and edges, and then locate the spots of changes in those features. We focus on using Robust Pixel Method for shot detection. The conventional shot detection method using pixel wise comparison is not very efficient since it doesn't provide noise tolerance and because of its global thresholding nature. Many scenes involving sudden illumination changes such as lighting etc false trigger a shot change in the conventional method. The robust pixel method used in this paper provides threshold for noise and also locally adaptive threshold which makes it effective in situations where the conventional method fails. Object tracking is an important task in the field of computer vision. It generates the path traced by a specified object by locating its position in each frame of the video sequence. The use of Object tracking is pertinent in many vision applications such as automated surveillance, video indexing, vehicle navigation, motion based recognition, security and defence areas. Occlusion and noise are generally the biggest problems in any target tracking implementation. Tracking algorithms robustness is a measure of how well it continues to track and when it loses its target. Tracking is the observation of person(s) or object(s) on the move and supplying a timely ordered sequence of respective location data to a model under consideration. It is the process of locating a moving human or object over time using a camera. It is based on computer vision. Image registration is the basic step used in tracking application. It is a process that finds the location where a good matching is obtained by matching the template over the searching area of an input image. Registration algorithm fails in complex situations and loses the target in presence of noise, scaling and transformation changes. To address the above mentioned problems, we use a particle filter based tracking method for efficient tracking. Video shot detection and tracking algorithms have both been extensively researched and have been used in real world applications individually. Very less effort has been made to combine the concepts of video shot detection and tracking which can be of great help in real-world as both the technologies complement each other. Combining the two concepts guarantees a computationally quicker, cost effective solution for tracking on large video database with minimal pre-processing. In this paper Section II covers the concepts of Video shot detection using robust pixel difference method and also demonstrates it effectiveness with results. Section III focuses on concepts of tracking algorithm with results. Section IV elaborates the method of combining the two approaches where tracking algorithm is initiated after every shot change hence serving its final purpose of computationally efficient shot detection cum tracking system.

To accelerate shot boundary detection by reducing detection region and scope

Video Shot Boundary Detection (SBD) is the fundamental process towards video summarization and retrieval. A fast and efficient SBD algorithm is necessary for real-time video processing applications. Extensive work has focused on accurate shot boundary detection at the expense of demanding computational costs. In this paper, we propose a fast SBD approach that reduces the computation pixel-wise and frame-wise while still giving satisfactory accuracy. The proposed approach substantially speeds up the computation through reducing both detection region and scope. Color histogram and mutual information are used together to measure the difference between frames. Corner distribution of frames is utilized to exclude most of false boundaries. We conduct extensive experiments to evaluate the proposed approach, and the results show that our approach can not only speed up SBD, but also detect shot boundaries with high accuracy in both Cut (CUT) and Gradual Transition (GT) boundaries.

Analysis of Popular Video Shot Boundary Detection Techniques in Uncompressed Domain

days there are tremendous amount of videos available on internet. Entertainment video, news video, sports video are accessed by users to fulfill their different needs. Our daily routine systems are also producing huge amount of videos for example surveillance system, shopping malls, home videos etc. These videos need to be accessed for different purposes. Current research topics on video includes video abstraction or summarization, video classification, video annotation, content based video retrieval. In nearly all these application one needs to identify shots and key frames in video which will correctly and briefly indicate the contents of video. This paper compares some of the popular shot boundary detection techniques in uncompressed domain. The merits and demerits of each of the techniques are also discussed. Some experiment done are also discussed.

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.

A Review of Machine-Vision-Based Analysis of Wireless Capsule Endoscopy Video

Wireless capsule endoscopy (WCE) enables a physician to diagnose a patient's digestive system without surgical procedures. However, it takes 1-2 hours for a gastroenterologist to examine the video. To speed up the review process, a number of analysis techniques based on machine vision have been proposed by computer science researchers. In order to train a machine to understand the semantics of an image, the image contents need to be translated into numerical form first. The numerical form of the image is known as image abstraction. The process of selecting relevant image features is often determined by the modality of medical images and the nature of the diagnoses. For example, there are radiographic projection-based images (e.g., X-rays and PET scans), tomography-based images (e.g., MRT and CT scans), and photography-based images (e.g., endoscopy, dermatology, and microscopic histology). Each modality imposes unique image-dependent restrictions for automatic and medically meaningful image abstraction processes. In this paper, we review the current development of machine-vision-based analysis of WCE video, focusing on the research that identifies specific gastrointestinal (GI) pathology and methods of shot boundary detection.

Shot Boundary Detection for a Video under the Influence of Illumination: A Adaptive Thresholding Approach

Video Shot boundary detection is the process of automatically detecting the boundaries between shots in video. Shot Boundary detection is an important fundamental process in video data access, indexing, search and retrieval. The increased availability and usage of on-line digital video has created a need for automated video content analysis techniques. Detection of gradual transition and elimination of disturbances caused by illumination change is a major challenge in shot boundary detection technique. These disturbances are often mistaken as shot boundaries. It is a crucial task to develop a method that is not only insensitive to illumination change but also sensitive to detect shot change. An algorithm is proposed for shot boundary detection in presence of illumination change. This is very important for accurate detection of shot boundaries and very useful in content based analysis of video. First the algorithm removes illumination change using discrete cosine transform and discrete wavelet transform. Then shot boundaries are detected using normal difference & wavelet difference. A shot boundary is detected when the feature difference shows sharp change greater than threshold. Experimental study is performed on number of videos that include significant illumination change. The performance of proposed algorithm is better as compared to existing techniques

An adaptive video shot segmentation scheme based on dual-detection model

Efficient segmentation of the video shots is the important and foundational work for the research of video content retrieval and analysis. A video shot segmentation scheme based on a dual-detection model is proposed, which includes the pre-detection and re-detection processes. The concepts of uneven blocked color histogram difference and uneven blocked pixel value difference based on human visual features are introduced, which are used as the main descriptors of the pre-detection process to enlarge the importance of central areas and to reduce the noises of background movements and logos. And the adaptive binary search method is introduced to fast detect boundaries with a time complexity of O(log2n). In the re-detection round, the scale invariant feature transform is applied to re-detect boundaries so as to improve the detection precision rate. Experiments show that this algorithm can improve both the recall rate and the precision rate of video shot boundary detection, especially for gradual shot boundaries.

Transition Detection Using Hilbert Transform and Texture Features

In this paper, we propose a new method for detecting shot boundaries in video sequences by performing Hilbert transform and extracting feature vectors from Gray Level Co-occurrence Matrix (GLCM). The proposed method is capable of detecting both abrupt and gradual transitions such as dissolves, fades and wipes in the video sequences. The derived features on processing through Kernel k-means clustering procedure results in efficient detection of abrupt and gradual transitions, as tested on TRECVid video test set containing various types of shot transition with illumination effects, object and camera movement in the shots. The results show that the proposed method yields better result compared to that of the existing tran- sition detection methods.

A Novel Algorithm for Key Frames Selection

The main objective of this study framework is to help the user to management the interesting video images from the huge storage device. The study can be divided into three main steps is listing as follows: shot boundary detection, key frame selection and evaluation method are designed by analyzing the video content. In this paper we proposed a novel algorithm for shot boundary detection. First, transform the RGB color model into LUV color model according our experimental results. The useful features of each frame are extracted from sequence images by the automatically computation of their color moment-preserving and matching 64-predefined bit planes in order to detect the shot boundary. The excellent shot boundary detection will result in the good key frames selection. Second, the key frame selection is proceed by applying the geometric invariant moments which successful be used in target tracing, pattern recognition, and facial recognition. In our experiments we used five test sequences that belong to five different program categories: movies, advertising, sport game, news, and commercial documentary. The experimental results showed that our method is feasible, effective, and is better than the former approaches.

Research for Algorithm of Shot Boundary Detection Robustness Based on Fuzzy Clustering

In order to reduce the effects caused by flash, subtitles, camera movement and other factors on shot detection, the method of segmenting images using histogram is improved. The frame difference between blocks are used as characters, and the weight of the blocks are used as feature weight to do fuzzy clustering . The images are pretreated according to the characteristics such as video mutation and gradually changing border. The effect that the factors of flash, subtitles, camera movement etc have on the shot boundary detection is excluded, finally the shot boundary detection is realized. The experiment shows that , whatever the video is ,the method has relatively strong robustness.

English

Video-surveillance systems are one of the main source of information during investigations. However, the adopted closed-circuit devices are often affected by poor quality mainly because of economical and practical problems. In many cases a low quality image can give useful information during the first phase of the investigation. As a consequence, the images and sequences coming from video-surveillance systems need to be digitalized in order to be processed to enhance and extract features useful for crime analysis. Visual information is the most appealing and intuitive mode of conveying information. Further, the amount of information that video carries is significantly greater than that carried by any other media. From surveillance for security, to capturing and analyzing medical procedures, to forensic applications. The greatest users of video information constitute the surveillance cameras used for security. The content in such applications constitutes hours of video usually taken from a single camera that is stationary. For such applications, it is usually necessary to trace certain anomalous events or individuals .In particular, airport surveillance cameras, surveillance camera in traffic, shopping malls are very good examples. The purpose of these cameras is to detect suspicious activity, like a perpetrator leaving a piece of luggage that might be harmful, or picking up something that might not belong to him or her, Firing from a vehicle, suspicious activity in shopping malls etc. In this work, the task is the automatic segmentation of continuous video into activity-based segments. It is different from the conventional shot-detection and scene change problems of video processing. The information being searched for is not the boundary between two different video contents in a single video, but a significant change in the activity within a single scene in the video. Thus, a temporal segmentation of the video into different events is desired. The approach used is to combine some of the conventional shot-detection methods with some new techniques for video-content analysis . KeywordsVideo-surveillance systems, crime analysis, automatic segmentation, activity-based segments, video-content analysis. International Journal of Machine Intelligence ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 4, Issue 1, 2012 Citation: Angadi S.A., Vilas Naik and Ashwin Kumar (2012) Automatic Activity Segmentation from Surveillance Video Using Conventional Shot Boundary Detection Methods. International Journal of Machine Intelligence, ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 4, Issue 1, pp.-404. Copyright: Copyright©2012 Angadi S.A., Vilas Naik and Ashwin Kumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Scalable shot boundary detection

In a Content-based Video Retrieval system, the shot boundary detection is an unavoidable stage. Such a high demanding task needs a deep study from a computational point of view to allow finding suitable optimization strategies. This paper presents different strategies implemented on both a shared-memory symmetric multiprocessor and a Beowulf cluster, and the evaluation of two different programming paradigms: shared-memory and message passing. Several approaches for video segmentation as well as data access are tested in the experiments that also consider load balancing issues.

Robust Video Content Analysis via Transductive Learning

Reliable video content analysis is an essential prerequisite for effective video search. An important current research question is how to develop robust video content analysis methods that produce satisfactory results for a large variety of video sources, distribution platforms, genres, and content. The work presented in this article exploits the observation that the appearance of objects and events is often related to a particular video sequence, episode, program, or broadcast. This motivates our idea of considering the content analysis task for a single video or episode as a transductive setting: the final classification model must be optimal for the given video only, and not in general, as expected for inductive learning. For this purpose, the unlabeled video test data have to be used in the learning process. In this article, a transductive learning framework for robust video content analysis based on feature selection and ensemble classification is presented. In contrast to related transductive approaches for video analysis (e.g., for concept detection), the framework is designed in a general manner and not only for a single task. The proposed framework is applied to the following video analysis tasks: shot boundary detection, face recognition, semantic video retrieval, and semantic indexing of computer game sequences. Experimental results for diverse video analysis tasks and large test sets demonstrate that the proposed transductive framework improves the robustness of the underlying state-of-the-art approaches, whereas transductive support vector machines do not solve particular tasks in a satisfactory manner.

Shot boundary detection and key-frame extraction from neurosurgical video sequences

In this work, we present a system for video shot boundary detection and key-frame extraction from video sequences based on colour histograms. The growth of archived video material has made automated indexing and browsing tools necessary. The aim of this paper is to provide a framework for such a video indexing and browsing tool. The proposed system segments video sequences based on colour similarity, and then extracts key-frames by clustering the images. Computations of colour histogram differences and self-similarity modelling have been performed in segmentation process. We have extracted three-, four- and five-frames video summaries with unsupervised k-means clustering. We have also dynamically determined the number of key-frames representing the video content without any prior information based on dominant set clustering. Our results show that our approach is successful in both segmentation and key-frame extraction processes.

A Model-Based Shot Boundary Detection Technique Using Frame Transition Parameters

We have presented a unified model for detecting different types of video shot transitions. Based on the proposed model, we formulate frame estimation scheme using the previous and the next frames. Unlike other shot boundary detection algorithms, instead of properties of frames, frame transition parameters and frame estimation errors based on global and local features are used for boundary detection and classification. Local features include scatter matrix of edge strength and motion matrix. Finally, the frames are classified as no change (within shot frame), abrupt change, or gradual change frames using a multilayer perceptron network. The proposed method is relatively less dependent on user defined thresholds and is free from sliding window size as widely used by various schemes found in the literature. Moreover, handling both abrupt and gradual transitions along with non-transition frames under a single framework using model guided visual feature is another unique aspect of the work.