Video Shot Boundary Detection Algorithm

Ko, Kyong-Cheol; Cheon, Young Min; Kim, Gye-Young; Choi, Hyung Woo; Shin, Seong-Yoon; Rhee, Yang-Won

doi:10.1007/11949619_35

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Changes in content always take place where two shots meet. A video sequence can be divided into shots for video summary and indexing was clarified in Ko et al, [20]. Shot boundary detection technique detects a shot cut if the scaled frame difference between two subsequent frames is more than a maxthreshold ( th max), if the contiguous frame difference value is greater than a k-threshold ( k gloval), and if the Euclidian distance is greater than or equal to a global threshold ( th gloval).…”

Section: Shot Boundary Detection Algorithmmentioning

confidence: 99%

Video Structure Extraction using Shot Boundary Detection for Forgery Detection

Noraida Haji Ali,

Fadilah Harun

2023

ARASET

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In the digital era, video and pictures are perceived as more reliable than words. Considering the popularity of the internet, forging digital videos is probably nothing new. The recording, archiving, and sharing images and videos on media platforms are frequently done with mobile phones and digital cameras. Additionally, anyone may easily adjust or edit an image because of the availability of software editors. Technology and technological advancements have made video authentication detection a prevalent issue. As a result, digital recordings are being used as visual evidence in many susceptible domains, including media, politics, civil or criminal trials, and military and intelligence activities. Digital videos are an essential source of information with a high degree of evidence, but they may also be easily manipulated on purpose. As a result, in situations where reliance on video evidence is required, it is crucial to authenticate the contents of the video evidence before accepting it as an accurate picture of reality. This research focuses on several strategies for detecting shot boundaries to determine whether video content has been altered or tampered with. Although shot boundary detection is difficult, it is commonly used in structured video because these techniques can detect changes brought on by a regular shot change rate. In the meantime, this research will provide a novel shot boundary detection-based video authentication algorithm essential for developing practical tools to evaluate the video's authenticity based on its fundamental structure.

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Section: Shot Boundary Detection Algorithmmentioning

confidence: 99%

Video Structure Extraction using Shot Boundary Detection for Forgery Detection

Noraida Haji Ali,

Fadilah Harun

2023

ARASET

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“…The shot boundary detection approaches are usually used:  Threshold approach: similarity between frames is compared with a predefined threshold [11].  Statistical learning approach: detection boundary of shot is considered as classification task, where supervised learning like SVM [12] and unsupervised learning like fuzzy k-means [13] are employed.…”

Section: Shot Boundary Detectionmentioning

confidence: 99%

Semantic Based Video Retrieval System: Survey

Abdulmunem

Hato

2018

ijs

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In this review paper a number of studies and researches are surveyed, in order to assist the upcoming researchers, to know about the techniques available in the field of semantic based video retrieval. The video retrieval system is used for finding the users' desired video among a huge number of available videos on the Internet or database. This paper gives a general discussion on the overall process of the semantic video retrieval phases. In addition to its present a generic review of techniques that has been proposed to solve the semantic gap as the major scientific problem in semantic based video retrieval. The semantic gap is formed because of the difference between the low level features that are extracted from video content and user's perceptions of these features in a real world. The transformation of low level features of the video content into meaningful semantic concepts is a research topic that has received considerable attention in recent years.

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“…Each video shot represents a continuous series of actions, which corresponds to a series of frames captured by single camera without significant changes in visual content [1]. The basic principle of shot detection algorithms is to find the boundary between the shots using video content features, where the boundary is the discontinuity frame(s) that describes the transition from one shot to the subsequent shot [2]. Shot boundary fall into two classes [3]: Abrupt Transition (AT) and Gradual Transition (GT).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Corner Feature Detectors for Video Abrupt Shot Detection

Abdulmunem

Hato

2018

JNUS

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Comparison of feature detectors and evaluation of their performance is very important in computer vision. A new algorithm is proposed in this paper to compare the performance of four corner feature detectors based on abrupt shot boundary detection. The proposed algorithm consists of two stages: feature vectors generation where corner detector for all video frames is computed to obtain the descriptor feature vectors, and features matching where the number of matching features between two successive frames is calculated. The corner feature detectors used in this paper are BRISK, Harries, MinEigen, and FAST. Experimental results indicate that the proposed algorithm using MinEigen features detector provides better performance than other features detectors where the average value of recall, precision, and F measure is 0.99083, 0.98808, and 0.98875 for selected testing videos respectively. The results also show that the FAST is superior to others feature detectors when considering execution time.

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Video Shot Boundary Detection Algorithm

Cited by 6 publications

References 10 publications

Video Structure Extraction using Shot Boundary Detection for Forgery Detection

Video Structure Extraction using Shot Boundary Detection for Forgery Detection

Semantic Based Video Retrieval System: Survey

A Comparison of Corner Feature Detectors for Video Abrupt Shot Detection

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