A Comparative Analysis of Video Shot Boundary Detection using Different Approaches

Chavate, Shrikant; Mishra, Ravi Shankar; Yadav, Pranay

doi:10.1109/smart52563.2021.9676246

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…The CT lung pictures are then classified once the image has been converted to a vector. The output of FCL is described by equation (16).…”

Section: Classification Of Shot Transitionsmentioning

confidence: 99%

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Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Chavate,

Mishra,

Singh

et al. 2023

IJST

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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.

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“…The CT lung pictures are then classified once the image has been converted to a vector. The output of FCL is described by equation (16).…”

Section: Classification Of Shot Transitionsmentioning

confidence: 99%

“…Performance metrics such as precision, recall rate, and F1 Score are used in this analysis; these three factors will evaluate the algorithm's usefulness and reliability (16,17) .…”

Section: Dtcwt-wht With Dbn-ssdoa Approachmentioning

confidence: 99%

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Chavate,

Mishra,

Singh

et al. 2023

IJST

View full text Add to dashboard Cite

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“…Various researchers have studied shot detection algorithms and established methods for identifying shot boundaries using a variety of features [ 5 , 6 , 7 ]. Given the similarity of frames within each shot, studying shallow visual features such as color histograms or tracking changes in mutual information of consecutive frames can deliver results on par with similar studies of deep features extracted from pre-trained object classification or action recognition models [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Video Scene Detection Using Transformer Encoding Linker Network (TELNet)

Tseng

Yeh

et al. 2023

Sensors

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This paper introduces a transformer encoding linker network (TELNet) for automatically identifying scene boundaries in videos without prior knowledge of their structure. Videos consist of sequences of semantically related shots or chapters, and recognizing scene boundaries is crucial for various video processing tasks, including video summarization. TELNet utilizes a rolling window to scan through video shots, encoding their features extracted from a fine-tuned 3D CNN model (transformer encoder). By establishing links between video shots based on these encoded features (linker), TELNet efficiently identifies scene boundaries where consecutive shots lack links. TELNet was trained on multiple video scene detection datasets and demonstrated results comparable to other state-of-the-art models in standard settings. Notably, in cross-dataset evaluations, TELNet demonstrated significantly improved results (F-score). Furthermore, TELNet’s computational complexity grows linearly with the number of shots, making it highly efficient in processing long videos.

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Video shot-boundary detection: issues, challenges and solutions

Kar,

Kanungo,

Mohanty

et al. 2024

Artif Intell Rev

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The integration of high data transmission rates and the recent digital multimedia technology, paves the way to access a huge amount of video over the internet, in seconds. Additionally, uploading videos to different websites is no more confined to expert software professionals resulting in duplication of video data which led to exorbitant growth of multimedia information in cyberspace in a short span of time. This necessitates the development of efficient data management techniques including storage, searching and annotation mechanism. Automatic shot boundary detection is considered to be the first and foremost step towards such management. It is a booming area of research gaining attention in the domain of image processing, computer vision and pattern recognition. In this review paper, we present a detailed description of the methods and algorithms of shot boundary detection, reported in the last two decades. This review shows that using multiple features performs well in comparison to using only a single feature in the shot boundary detection problem although it leads to higher complexity. The major sources of disturbance in the boundary detection are the sudden illumination variation and presence of high motion in the video. An adaptive threshold outperforms a single global threshold in the boundary detection problem and the threshold requirement can be avoided through learning based strategies at the cost of larger training data and higher computation time. Moreover the present review includes a critical analysis of relative merits and demerits of existing algorithms and finally opens promising research directions in the area.

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A Comparative Analysis of Video Shot Boundary Detection using Different Approaches

Cited by 12 publications

References 29 publications

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Video Scene Detection Using Transformer Encoding Linker Network (TELNet)

Video shot-boundary detection: issues, challenges and solutions

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