Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Shrikant Chavate; Ravi Mishra; S K Singh; Dinesh Sharma

doi:10.17485/IJST/v16i33.1671

Article

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i33.1671

Year: 2023, Volume: 16, Issue: 33, Pages: 2670-2680

Original Article

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Shrikant Chavate^1*, Ravi Mishra², S K Singh³, Dinesh Sharma⁴

¹Ph.D. Scholar, Department of Electronics and Telecommunication Engineering, G H Raisoni University, Amravati, India
²Associate Professor, Department of Electronics and Telecommunication Engineering, G H Raisoni Institute of Engineering and Technology, Nagpur, India
³Professor, School of Digital Technology, Atlas Skill Tech University, Mumbai, India
⁴Assistant Professor, Department of Electronics and Telecommunication Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India

*Corresponding Author
Email: [email protected]

Received Date:07 July 2023, Accepted Date:29 July 2023, Published Date:08 September 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: The objective of this work is to detect video shot boundary which is an important phase in the domain of video processing. The applications such as indexing, retrieval and browsing of video are majorly depends on accurate detection of shot boundary so the detection is highly necessary in the area of video processing. Methods: Many researchers have contributed to the field of Shot Boundary Detection (SBD) to improve video processing throughout the previous decade. Some of the techniques have detected abrupt as well as gradual transitions with remarkable performance metrics values, but they compromised with the execution time and operating speed. Some of the techniques proposed and implemented the simpler approaches, but failed to detect the gradual transitions. Additionally, the effects like illuminations and camera motion lead to produce false results. Thus, taking these aspects into consideration, the novel approach for SBD has to be implemented. Findings: In this paper, fusion of transform and filtering at an early stage has been applied for reducing the false hits that occurs due to illumination noise. The fusion of transform with appropriate classifier enhances the detection performance. Another deep learning based technique with Rider bald eagle search optimization given the solution for the SBD. Novelty and applications: The proposed two approaches give the detection of abrupt and gradual transitions. It has been noticed that the said approaches found effective against the illuminations. To evaluate the performance it uses precision, recall and F1 score as performance metrics. The experiments are performed on latest TRECVID datasets and few open source video datasets.

Keywords: Shot Boundary Detection; Walsh Hadamard Transform (WHT); Dual Tree Complex Wavelet Transform; Deep Belief Network; Deep Convolutional Neural Network

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Copyright

© 2023 Chavate et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)