RELIABLE TRANSITION DETECTION IN VIDEOS: A SURVEY AND PRACTITIONER'S GUIDE

2001 ◽  
Vol 01 (03) ◽  
pp. 469-486 ◽  
Author(s):  
RAINER LIENHART
Keyword(s):  
Video Processing ◽  
Boundary Detection ◽  
Shot Boundary Detection ◽  
Detection Techniques ◽  
Core Concepts ◽  
Reliable Performance ◽  
Shot Boundary ◽  
Transition Effects ◽  
Detection Schemes

A large number of shot boundary detection, or equivalently, transition detection techniques have been developed in recent years. They all can be classified based on a few core concepts underlying the different detection schemes. This survey emphasizes those different core concepts underlying the different detection schemes for the three most widely used video transition effects: hard cuts, fades and dissolves. Representative of each concept one or a few very sound and thoroughly tested approaches are present in detail, while others are just listed. Whenever reliable performance numbers could be found in the literature, they are mentioned. Guidelines for practitioners in video processing are also given.

Shot Boundary Detection Techniques for Video Sequences

2007 ◽  
pp. 889-893
Author(s):  
H. Xilouris Koumaras
Keyword(s):  
Change Detection ◽  
Boundary Detection ◽  
Video Sequences ◽  
Shot Boundary Detection ◽  
Scene Change ◽  
Scene Change Detection ◽  
Detection Techniques ◽  
Shot Boundary

This chapter will outline the various existing methods of boundary shot and scene change detection.

Fast Video Shot Boundary Detection Technique based on Stochastic Model

2016 ◽  
Vol 6 (2) ◽  
pp. 1-17
Author(s):  
Mohammad A. Al-Jarrah ◽  
Faruq A. Al-Omari
Keyword(s):  
Stochastic Model ◽  
Video Processing ◽  
High Performance ◽  
Boundary Detection ◽  
Video Stream ◽  
Detection Algorithm ◽  
Shot Boundary Detection ◽  
Different Types ◽  
Video Shot ◽  
Shot Boundary

A video is composed of set of shots, where shot is defined as a sequence of consecutive frames captured by one camera without interruption. In video shot transition could be a prompt (hard cut) or gradual (fade, dissolve, and wipe). Shot boundary detection is an essential component of video processing. These boundaries are utilized on many aspect of video processing such as video indexing, and video in demand. In this paper, the authors proposed a new shot boundary detection algorithm. The proposed algorithm detects all type of shot boundaries in a high accuracy. The algorithm is developed based on a global stochastic model for video stream. The proposed stochastic model utilizes the joined characteristic function and consequently the joined momentum to model the video stream. The proposed algorithm is implemented and tested against different types of categorized videos. The proposed algorithm detects cuts fades, dissolves, and wipes transitions. Experimental results show that the algorithm has high performance. The computed precision and recall rates validated its performance.

A Comparative Study of Unsupervised Video Shot Boundary Detection Techniques Using Probabilistic Fuzzy Entropy Measures

2013 ◽  
pp. 228-240 ◽  
Author(s):  
Biswanath Chakraborty ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Boundary Detection ◽  
Fuzzy Entropy ◽  
Detection Technique ◽  
Shot Boundary Detection ◽  
Detection Techniques ◽  
Entropy Measures ◽  
Video Shot ◽  
Shot Boundary ◽  
Improved Performance ◽  
A New Technique

The performance of video shot boundary detection technique in unsupervised video sequence can be improved by the use of different probabilistic fuzzy entropies. In this chapter, the authors present a new technique for identifying as to whether there are any appreciable changes from one video context to another in the available sequence of image frames extracted from a mixture of a numbers of video files. They then compared their technique with an existing technique and found improved performance of the video shot boundary detection techniques using probabilistic fuzzy entropies.

An Improved Algorithm of Video Shot Boundary Detection

2011 ◽  
Vol 403-408 ◽  
pp. 1258-1261
Author(s):  
Jian Feng Zhang ◽  
Zhi Qiang Wei ◽  
Shu Ming Jiang ◽  
Jian Li ◽  
Shi Jie Xu ◽  
...  
Keyword(s):  
Error Rate ◽  
Video Processing ◽  
Boundary Detection ◽  
Local Information ◽  
Color Histogram ◽  
Shot Boundary Detection ◽  
Global Information ◽  
Threshold Algorithm ◽  
Video Shot ◽  
Shot Boundary

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.

Generative Model Based Video Shot Boundary Detection for Automated Surveillance

2018 ◽  
Vol 9 (4) ◽  
pp. 69-95 ◽  
Author(s):  
Biswanath Chakraborty ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Video Processing ◽  
Contextual Information ◽  
Boundary Detection ◽  
Gaussian Mixture ◽  
Generative Model ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Model Based ◽  
Video Shot ◽  
Shot Boundary

Video shot boundary detection (SBD) or video cut detection is one of the fundamental processes of video-processing with respect to semantic understanding, contextual information accumulation, labeling, content-based information retrieval and many more applications, such as video surveillance and monitoring. In this work, the authors have proposed a generative-model based framework for detecting shot boundaries in between the frames of a video segment. To generate a model of shot-boundaries, the authors have applied the concepts of Support Vector Machine to estimate the distance between any two images, and then, have generated a Gaussian Mixture Model from the estimated distances. Next, a Bayesian Estimation process checks the presence of boundaries in between the images by exploiting the Gaussian Mixture-based boundary model. Further, the authors have used the principles of Compressive Sensing to reduce the overhead of boundary detection process without losing of important information.

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