Video Shot Boundary Detection: A Review (original) (raw)

Lecture Notes in Computer Science, 2006

We present a newly developed algorithm for automatically segmenting videos into basic shot units. A basic shot unit can be understood as an unbroken sequence of frames taken from one camera. At first we calculate the frame difference by using the local histogram comparison, and then we dynamically scale the frame difference by Log-formula to compress and enhance the frame difference. Finally we detect the shot boundaries by the newly proposed shot boundary detection algorithm which it is more robust to camera or object motion, and many flashlight events. The proposed algorithms are tested on the various video types and experimental results show that the proposed algorithm are effective and reliably detects shot boundaries.

Video Shot Boundary Detection Using Various Techniques

2015

Shot and classification is first and foremost step for further analysis of video content. A Shot is defined as a set of frames from a single camera. Processing of video and image facilitates better understanding of the scene that it describe. It is a fundamental component of a number of technologies like video surveillance, robotics etc .A scene is a collection of one or more shots focusing on one or more object of interest. various shot boundary methods have been developed which can detect cut and gradual shot simultaneously.

Video Shot Boundary Detection

Video data management and information …, 2005

The increasing use of multimedia streams nowadays necessitates the development of efficient and effective methodologies for manipulating databases storing this information. Moreover, in its first stage, content-based access to video data requires parsing of each video stream into its building blocks. The video stream consists of a number of shots, each one a sequence of frames pictured using a single camera. Switching from one camera to another indicates the transition from a shot to the next one. Therefore, the detection of these transitions, known as scene change or shot boundary detection, is the first step in any video-analysis system. A number of proposed techniques for solving the problem of shot boundary detection exist, but the major criticisms to them are their inefficiency and lack of reliability. The reliability of the scene change detection stage is a very significant requirement because it is the first stage in any video retrieval system; thus, its performance has a direct impact on the performance of all other stages. On the other hand, efficiency is also crucial due to the voluminous amounts of information found in video streams. This chapter proposes a new robust and efficient paradigm capable of detecting scene changes on compressed MPEG video data directly. This paradigm constitutes the first part of a Video Contentbased Retrieval (VCR) system that has been designed at Old Dominion University. At first, an abstract representation of the compressed video stream, known as the DC

Evaluating and combining digital video shot boundary detection algorithms

2000

The development of standards for video encoding coupled with the increased power of computing mean that content-based manipulation of digital video information is now feasible. Shots are a basic structural building block of digital video and the boundaries between shots need to be determined automatically to allow for content-based manipulation. A shot can be thought of as continuous images from one camera at a time. In this paper we examine a variety of automatic techniques for shot boundary detection that we have implemented and evaluated on a baseline of 720,000 frames (8 hours) of broadcast television. This extends our previous work on evaluating a single technique based on comparing colour histograms. A description of each of our three methods currently working is given along with how they are evaluated. It is found that although the different methods have about the same order of magnitude in terms of effectiveness, different shot boundaries are detected by the different methods. We then look at combining the three shot boundary detection methods to produce one output result and the benefits in accuracy and performance that this brought to our system. Each of the methods were changed from using a static threshold value for three unconnected methods to one using three dynamic threshold values for one connected method. In a final summing up we look at the future directions for this work.

Shot Boundary Detection Using Shifting of Image Frame

Detection of shot boundary is the key step for identification of visual content of the video data. In this paper we propose a method of shot boundary detection with a novel logic of frame comparison. In our method, current frame of the video data is compared with the shifted version of the previous frame of that video and by using suitable threshold, shot boundary is declared. The experimental results show that the obtained results are more effective and accurate than the existing methods.

A Classification Approach to Video Shot Boundary Detection

International Journal of Signal Processing, Image Processing and Pattern Recognition, 2017

Video content retrieval just like information retrieval requires some pre-processing such as indexing, key-frame selection and most importantly accurate video shot boundary detection. Accurate detection of video shots give way for video information to be stored in a manner that will allow easy access. Several algorithms have been developed in this field of study and tested even at the TRECVID 2002, 2005 and 2007 tasks evaluation conferences. Challenges on accurate detection of these different types of video transitions have always been from large object and camera motions as well as fast zooming, flashlights, and change in luminance. These attributes differ from one video sequence to another and features of one video sequence cannot always match with features of another video. Therefore, in our work we use a video specific machine learning approach that leverages information from several shot boundary detection algorithms in order to improve the detection of the shot boundaries on a video sequence. Our results suggest that a classifier built from a combination of block-based motion estimation, RGB histogram based block-based cross-correlation coefficient and RGB histogram based sum of squared difference provided better results with an average F1 score of 0.752 on shot boundary detection for the seventeen videos tested. This proves that a combination of luminance and motion based algorithms improves the detection of video shot boundaries. We also found that the detection of shot cuts and gradual transitions can be improved by using features generated by several shot boundary detection algorithms.

Now 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.

An integrated method for video shot boundary detection

Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon), 2010

Video shot boundary detection, which segments a video by detecting boundaries between camera shots, is usually the first and important step for content-based video retrieval. This paper investigates methods which are effective in detecting abrupt transitions and gradual transitions, respectively, and proposes an integration scheme to combine their results, aiming to detect both types of transitions.

Video shot boundary detection by structural analysis of local image features

2011

ABSTRACT In this paper a novel shot boundary detection (SBD) algorithm is proposed in order to detect both abrupt and gradual transitions. Visual content changes between different shots are detected via structural analysis of local image features. A top-down approach is utilized in order to find the location of the transition accurately, while keeping the computational load minimal avoiding unnecessary feature extraction and matching.

Video Shot Boundary Detection Techniques

2014

Shot boundary detection (SBD) is the basis of revealing even higher levels of the hierarchical video structure. If we advance further from the scene level, we can try to extract even higher-level semantical information from the video by using the shot boundary information. The next structural level above the shot and scene levels in news are videos, story level which contains stories that are made of several scenes. The story boundaries thus usually overlap with some of the scene boundaries, so gathering information on the shot boundaries and scene boundaries help to solve the problem. This bottom-up approach might help us eventually to narrow the semantic gap a little bit more. Knowing the shot boundaries is also crucial for some lower-level feature extraction methods. For example to figure the system which solve the camera movement based on the video data and differences between consecutive frames. This camera movement detection is naturally sensible only inside shot boundaries si...

Video Shot Boundary Detection: A Review (original) (raw)

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