A novel bifold-stage shot boundary detection algorithm: invariant to motion and illumination

Chakraborty, Saptarshi; Singh, Alok; Thounaojam, Dalton Meitei

doi:10.1007/s00371-020-02027-9

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…Sports videos use the research hotspots in many fields, such as image processing of panorama synthesis technology, human simulation, and computer vision. Taking athletes as research objects, qualitative and quantitative analysis is carried out to achieve the purpose of scientific training [4]. The solutions to global motion analysis problems can be divided into two categories, namely, differential method and feature comparison method.…”

Section: Introductionmentioning

confidence: 99%

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

Chen

2022

Wireless Communications and Mobile Computing

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The development requirements for sports panorama synthesis technology, which rely on modern network technology, abandon traditional basketball training forms, and make effective use of the application and development of video panorama technology in the actual training process, are not only an important response to the current characteristics of students’ physical education learning and physical and mental growth but also an important response to the current characteristics of students’ physical education learning and physical and mental growth. In sports video analysis technology, a sports video panorama is a technical tool that converts an action video into a static action panorama to achieve the effect of action freezing and make overall analysis and mastery of the action easier. Shot segmentation is the foundation of hierarchical video structure, and it necessitates the accurate detection of all types of complex edited shot boundaries, as well as the effective distinction of motion changes in shots, in order to avoid shot boundary recognition being hampered. The synthesis technology for sports video panorama is investigated in this paper using edge computing and video shot boundary detection. After obtaining the boundary feature that describes the video shot, a comparison of this feature with a predetermined threshold value can be used to determine whether there is shot shear.

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Section: Introductionmentioning

confidence: 99%

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

Chen

2022

Wireless Communications and Mobile Computing

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“…methods. SBD with nonsubsampled contourlet transform [27], separable moments, and SVM based SBD [20], SBD using block-based cumulative approach [7], local binary pattern histogram Fourier based SBD [24], SBD using pyramidal opponent colour shape [28], and a bifold-stage SBD [29] are considered for comparison. Table 6 shows the comparison result of the recall, precision, and F1 score.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Efficient Shot Boundary Detection with Multiple Visual Representations

Jose

Rajkumar

Ghalib³

et al. 2022

Mobile Information Systems

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Due to the unlimited growth of video-capturing devices and media, searching and finding a particular video in this huge database becomes a laborious as well as expensive task. Information-rich shots are the inevitable factor of the content-based video processing (CBVP) system. Hence, shot boundary detection (SBD) becomes the basic step of all content-based video retrieval processes. The accuracy of the existing SBD methods highly suffers from false positives and false negatives due to the presence of multiple variants. An efficient SBD method with multiple invariant features is proposed in this paper. A right combination of invariant features such as edge change ratio (ECR), colour layout descriptor (CLD), and scale-invariant feature transform (SIFT) key point descriptors helped to improve the accuracy level of SBD. As the selected features are invariant to most of the variants in video frames, such as illuminance changes, motion, scaling, and rotation, a markable reduction in false detection is possible. Support vector machine (SVM) classifier is used for the classification of frames into transition frames and shot frames. This proposed method is experimented and analysed with the standard SBD dataset TRECVid 2007 videos. The experimental results are compared with some state-of-art methods, and our method shows better performance with a 97% of F1 score.

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“…One of the simplest methods of constructing this metric is a pixel-by-pixel comparison of frames [7]. Other difference metrics are also calculated between frames -for example, [16] uses L*a*b* space and the formula for the distance between colors in it.…”

Section: Related Workmentioning

confidence: 99%

Shot Boundary Detection Method Based on a New Extensive Dataset and Mixed Features

Gushchin¹,

Antsiferova²,

Vatolin³

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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Shot boundary detection in video is one of the key stages of video data processing. A new method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed. The developed algorithm was tested on the open BBC Planet Earth [1] and RAI [2] datasets, and the MSU CC datasets, based on videos used in the video codec comparison conducted at MSU, as well as videos from the IBM set, were also plotted. The total dataset for algorithm development and testing exceeded the known TRECVID datasets. Based on the test results, the proposed algorithm for scene change detection outperformed its counterparts with a final F-score of 0.9794.

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A novel bifold-stage shot boundary detection algorithm: invariant to motion and illumination

Cited by 14 publications

References 31 publications

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

Efficient Shot Boundary Detection with Multiple Visual Representations

Shot Boundary Detection Method Based on a New Extensive Dataset and Mixed Features

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