Video shot boundary detection using block based cumulative approach
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Video shot boundary detection using block based cumulative approach B. S. Rashmi 1,2 & H. S. Nagendraswamy 1 Received: 10 September 2019 / Revised: 18 August 2020 / Accepted: 21 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Video data is becoming an indispensable part of today’s Big Data due to evolution of social web and mobile technology. Content based video analysis has become crucial for video management. Shot boundary detection is one of the most essential task in video content analysis. In view of this, an efficient shot boundary detection approach to detect abrupt and gradual transition in videos is proposed in this work. The approach extracts block based Mean Cumulative Sum Histogram (MCSH) from each edge gradient fuzzified frame as a combination of local and global feature. The relative standard deviation (RSD) statistical measure is applied on the obtained MCSH to detect abrupt and gradual shots in the video. Efficacy of the proposed method is measured by conducting experiments on TRECVID 2001, TRECVID 2007 and VideoSeg datasets. The proposed method shows relatively a good performance when compared to some of the state-of-the-art shot boundary detection approaches. Keywords Block . Cumulative . Fuzzy set . Gradient . Histogram . Relative Standard Deviation . Threshold . Shot boundary detection
1 INTRODUCTION In the recent years Internet and social media platforms are ubiquitous and plethora of video information is generated in every single minute. With the proliferation of 5G technology and the advancement in smart phones, mobile users and Internet of Things (IoT) are predicted to increase mobile video data traffic. Development in video acquisition technologies has led to the creation of massive video repositories on storage platforms. The users may prefer to query videos based on the content instead of sequentially accessing the video data, which demands * B. S. Rashmi [email protected]–mysore.ac.in
1
DoS in Computer Science, University of Mysore, Mysore 570006, India
2
Department of Information Technology, Karnataka State Open University, Mysore 570006, India
Multimedia Tools and Applications
sophisticated technology for representing, indexing and retrieving multimedia data. Video management in manual mode is arduous and hence it is crucial to develop efficient algorithms to store, index and retrieve the videos. This domain of research is referred as Content Based Video Retrieval (CBVR) system. CBVR seem to be inherent extension of Content Based Image Retrieval (CBIR). CBVR system is the task of providing relevant video shots/clips as per the user query. The approaches and paradigms for CBVR must promote to align computer vision in line with human perceptions [8]. The term “content” stands for image features such as color, shape, texture etc. and the term “retrieval” refers to the techniques that fetch results in relation and accordance with user perception. Thus, CBVR can be imposed as the search for videos that matches the query given by the
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