Shot Boundary Detection Method Research Articles

Review of Research on Shot Boundary Detection Algorithm of the Compressed Video Domain in Content-based Video Retrieval Technique

Based on the background of extensive use of the video retrieval technique in substation monitoring, this paper focuses on the research of the shot boundary detection methods in the video retrieval technique. The research content is mainly involved in the following four kinds of shot boundary detection methods: the shot boundary detection method based on the base tone, the shot boundary detection method based on the macro- block motion information, the shot boundary detection method based on the motion vector and the video shot segmentation method based on clustering. Through the in-depth explanation and comparison of four kinds of shot boundary detection methods, this paper systematically summarizes the characteristics of four kinds of shot boundary detection methods, and also vividly points out the problems of four kinds of shot boundary detection methods, thus pointing out the direction for the future development of video retrieval technique.

Multi-Modal Visual Features-Based Video Shot Boundary Detection

One of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still remains its principal limitation. In this paper, a multi-modal visual features-based SBD framework is employed that aims to analyze the behaviors of visual representation in terms of the discontinuity signal. We adopt a candidate segment selection that performs without the threshold calculation but uses the cumulative moving average of the discontinuity signal to identify the position of shot boundaries and neglect the non-boundary video frames. The transition detection is structurally performed to distinguish candidate segment into a cut transition and a gradual transition, including fade in/out and logo occurrence. Experimental results are evaluated using the golf video clips and the TREC2001 documentary video data set. Results show that the proposed SBD framework can achieve good accuracy in both types of video data set compared with other proposed SBD methods.

Towards key-frame extraction methods for 3D video: a review

The increasing rate of creation and use of 3D video content leads to a pressing need for methods capable of lowering the cost of 3D video searching, browsing and indexing operations, with improved content selection performance. Video summarisation methods specifically tailored for 3D video content fulfil these requirements. This paper presents a review of the state-of-the-art of a crucial component of 3D video summarisation algorithms: the key-frame extraction methods. The methods reviewed cover 3D video key-frame extraction as well as shot boundary detection methods specific for use in 3D video. The performance metrics used to evaluate the key-frame extraction methods and the summaries derived from those key-frames are presented and discussed. The applications of these methods are also presented and discussed, followed by an exposition about current research challenges on 3D video summarisation methods.

A Method of Video Shot-Boundary Detection based on Grey Modeling for Histogram Sequence

A Method of Video Shot-Boundary Detection based on Grey Modeling for Histogram Sequence

An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram

In today’s digital era, there are large volumes of long-duration videos resulting from movies, documentaries, sports and surveillance cameras floating over internet and video databases (YouTube). Since manual processing of these videos are difficult, time-consuming and expensive, an automatic technique of abstracting these long-duration videos are very much desirable. In this backdrop, this paper presents a novel and efficient approach of video shot boundary detection and keyframe extraction, which subsequently leads to a summarized and compact video. The proposed method detects video shot boundaries by extracting the SIFT-point distribution histogram (SIFT-PDH) from the frames as a combination of local and global features. In the subsequent step, using the distance of SIFT-PDH of consecutive frames and an adaptive threshold video shot boundaries are detected. Further, the keyframes representing the salient content of each segmented shot are extracted using entropy-based singular values measure. Thus, the summarized video is then generated by combining the extracted keyframes. The experimental results show that our method can efficiently detect shot boundaries under both abrupt and gradual transitions, and even under different levels of illumination, motion effects and camera operations (zoom in, zoom out and camera rotation). With the proposed method, the computational complexity is comparatively less and video summarization is very compact.

A Self-Adaptive Shot Boundary Detection Method Based on Neighbor Frame Difference and Non-neighbor Frame Difference

A Self-Adaptive Shot Boundary Detection Method Based on Neighbor Frame Difference and Non-neighbor Frame Difference

Walsh–Hadamard Transform Kernel-Based Feature Vector for Shot Boundary Detection

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.

Analysis of Shot Boundary Based on Color and Texture Features of Frame

This paper proposed a shot boundary detection method based on color and texture features of frames. The shot boundary detection is employed in order to build a highly efficient method for video description. The research methods include feature extraction using color and texture feature, and shot boundary detection offering an adaptive measuring method. In this paper, the color and texture features are Traditional Color Histogram (TCH) and Histogram of Gradient Directions (HGD), respectively. The adaptive measuring method was proposed for the threshold of distance as the basis of identifying shot changes between frames. In order to validate the shot boundary detection method proposed herein, the video database provided by Carleton University website was used for the experiment. The experimental and comparison results of shot boundary detection were validated, and the experimental results were analyzed. This study carried out a series of comparisons and analyses for the above video database, and proved that the shot boundary detection method proposed could effectively detect the shot boundary.

A Shot Boundary Detection Method for News Video based Human Skin Region (Face) Detection

A New simple approach to detect, classify shot boundaries and store shot boundary frames in Video sequence using human skin region detection based approach is proposed. Human skin region detection is the process of detecting skin region in sequence of frames. Skin region detection is mainly used for the identification of the human face detection. This approach is very much suitable for finding shots in TV News so that we can classify anchor and non-anchor frames to save the overall time which is required to watch overall news,

화소 밝기와 객체 이동을 이용한 비디오 샷 경계 탐지 알고리즘

비디오 데이터를 효율적으로 검색, 정렬, 탐색, 분류하기 위해서는 프레임 간의 샷 전환 탐지가 선행되어야 한다. 프레임 간 화소 밝기와 객체 이동은 높은 탐지율을 보장하는 샷 탐지 알고리즘이 극복해야할 문제이다. 본 논문에서는 프레임의 블록화 및 객체의 이동과 프레임의 밝기를 고려하는 샷 탐지 방법을 제안한다. 먼저 연속하는 두 프레임 사이에서 발생할 수 있는 객체의 이동을 고려하여 계산된 히스토그램과 밝기 차이를 반영하는 모폴러지 팽창 연산을 이용하는 알고리즘을 제안한다. 다음으로 화소 밝기 차를 보상한 프레임 블록의 화소정보와 프레임의 전역적인 밝기 히스토그램의 변화를 함께 이용하는 샷 탐지 방법을 제안한다. 제안된 방법들은 국가기록원 소장 비디오 데이터에 대한 실험에서 화소 또는 히스토그램 기반 알고리즘에 비해 높은 샷 탐지율을 보였다. Shot boundary detection is an essential step for efficient browsing, sorting, and classification of video data. Robust shot detection method should overcome the disturbances caused by pixel brightness and object movement between frames. In this paper, two shot boundary detection methods are presented to address these problem by using segmentation, object movement, and pixel brightness. The first method is based on the histogram that reflects object movements and the morphological dilation operation that considers pixel brightness. The second method uses the pixel brightness information of segmented and whole blocks between frames. Experiments on digitized video data of National Archive of Korea show that the proposed methods outperforms the existing pixel-based and histogram-based methods.

Advanced Endoscopic Imaging

Advanced Endoscopic Imaging

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.

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.

Edge Strength Extraction using Orthogonal Vectors for Shot Boundary Detection

Content Based Video Retrieval mainly focuses on automatic indexing, retrieval and management of video data. The first step of the Content Based Video Retrieval is Shot Boundary Detection. In this paper, we propose a new method for detecting shot boundaries in video sequences by extracting edge strength using orthogonal vectors from blocks of the frames. Also, we propose transition (boundary) identification procedure which is capable of detecting the shot / non shot boundaries in the video sequences. The evaluation of the proposed method is performed by testing on TRECVid video test set containing various types of shot transition with lighting effects, object and camera movement within the shots. The results show that the proposed method yields better result compared to that of the existing shot boundary detection methods. Also, validation of the proposed method is performed using the performance indices like MSE, RMSE, R2.

An Improved Algorithm of Video Shot Boundary Detection

Shot boundary detection is the first step of the video processing. Based on the summary of the existing methods of shot boundary detection, this paper put forward an adaptive dual threshold algorithm which adjusts automatically the threshold along with the changes of the content of shots. Color histogram of weighted non-uniform blocks is taken as characteristic to calculate differences of frames in the algorithm, which considers not only the global information of the image, but also the local information. The second detection is carried on when the boundaries are detected initially reduces effectively error rate that caused by flashlight. Finally, the algorithm is proved effective.

A unified scheme of shot boundary detection and anchor shot detection in news video story parsing

In this paper, we propose an efficient one-pass algorithm for shot boundary detection and a cost-effective anchor shot detection method with search space reduction, which are unified scheme in news video story parsing. First, we present the desired requirements for shot boundary detection from the perspective of news video story parsing, and propose a new shot boundary detection method, based on singular value decomposition, and a newly developed algorithm, viz., Kernel-ART, which meets all of these requirements. Second, we propose a new anchor shot detection system, viz., MASD, which is able to detect anchor person cost-effectively by reducing the search space. It consists of skin color detector, face detector, and support vector data descriptions with non-negative matrix factorization sequentially. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

Shot boundary detection without threshold parameters

Automatic shot boundary detection is a field, where many techniques and methods have been proposed and have claimed to perform reliably, especially for abrupt scene cut detection. However, all the proposed methods share a common drawback: the necessity of a threshold value, which is used as a reference for detecting scene changes. The determination of the appropriate value or the dynamic reestimation of this threshold parameter remains the most challenging issue for the existing shot boundary detection algorithms. We introduce a novel method for shot boundary detection of discrete cosine transform (DCT)-based and low-bit-rate encoded clips, which exploits the perceptual blockiness effect detection on each frame without using any threshold parameter, therefore minimizing the processing demands required for algorithm implementation.

Information theory-based shot cut/fade detection and video summarization

New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses MI as well. We show that it captures satisfactorily the visual content of the shot.

An effective post-refinement method for shot boundary detection

In content-based video analysis, shot boundary detection (SBD) is a common first step which segments video data into elementary shots, each comprising a sequence of consecutive frames recording a video event or scene continuous in time and space. Many SBD methods have been proposed in the literature, and experimental results show that the existing methods work reasonably well for abrupt shot boundaries, but less effectively for gradual shot boundaries. In this paper, we propose an effective post-refinement method for identifying actual shot boundaries from the results obtained by existing SBD methods. The proposed method formulates the SBD problem as sequential detection of changes in the underlying feature distributions whose parameters are estimated from existing video shots. Specifically, the proposed post-refinement method enhances the performance of SBD by identifying as many false positives (false detections) and false negatives (miss detections) as possible. Experiments conducted on a large set of test videos, whose initial shot boundaries are obtained by four existing SBD methods, show that the proposed post-refinement method can improve markedly the detection recall and precision and is rather insensitive to the thresholds used by the existing methods in detecting the initial shot boundaries.