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Generative Model Based Video Shot Boundary Detection for Automated Surveillance

Generative Model Based Video Shot Boundary Detection for Automated Surveillance

Biswanath Chakraborty, Siddhartha Bhattacharyya, Susanta Chakraborty
Copyright: © 2018 |Volume: 9 |Issue: 4 |Pages: 27
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522543558|DOI: 10.4018/IJACI.2018100105
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MLA

Chakraborty, Biswanath, et al. "Generative Model Based Video Shot Boundary Detection for Automated Surveillance." IJACI vol.9, no.4 2018: pp.69-95. http://doi.org/10.4018/IJACI.2018100105

APA

Chakraborty, B., Bhattacharyya, S., & Chakraborty, S. (2018). Generative Model Based Video Shot Boundary Detection for Automated Surveillance. International Journal of Ambient Computing and Intelligence (IJACI), 9(4), 69-95. http://doi.org/10.4018/IJACI.2018100105

Chicago

Chakraborty, Biswanath, Siddhartha Bhattacharyya, and Susanta Chakraborty. "Generative Model Based Video Shot Boundary Detection for Automated Surveillance," International Journal of Ambient Computing and Intelligence (IJACI) 9, no.4: 69-95. http://doi.org/10.4018/IJACI.2018100105

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Abstract

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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