Abstract
With rapid development in Digital Video Technology capturing of video has become very easy and an integral part of our lives. Exponentially growing and already existing video data must be managed effectively. This paper, which is an extension of our previous research work, explores new distribution domains in addition to the distributions discussed in our earlier work in order to determine the best one for the purpose of detection of ‘cut’ transitions in videos. Simple shot detection algorithms have been used for finding the “cut” transition. Appropriate memory models have been used so that the algorithm runs seamlessly on the video. Finally, from the analysis of results we find out the distribution and video shot detection model which gives the best accuracy and efficiency.
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Acknowledgment
The authors express their sincere gratitude to Prof. N.R. Shetty, Advisor and Dr. H.C. Nagaraj, Principal, Nitte Meenakshi Institute of Technology, Bangalore for giving constant encouragement and support to carry out research at NMIT. The authors extend their thanks and gratitude to the Vision Group on Science and Technology (VGST), Government of Karnataka to acknowledge their research and providing financial support to setup the infrastructure required to carry out the research.
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Majumdar, J., Aniketh, M., Abhishek, B.R. (2019). Detection of Cut Transition of Video in Transform Domain. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_17
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DOI: https://doi.org/10.1007/978-3-030-01174-1_17
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