Automatic Video Summarization from Cricket Videos Using Deep Learning

Emon, Solayman Hossain; Annur, A.H.M; Xian, Abir Hossain; Sultana, Kazi Mahia; Shahriar, Shoeb Mohammad

doi:10.1109/iccit51783.2020.9392707

Cited by 9 publications

(1 citation statement)

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“…The measures used for evaluation on the Office dataset are recall, precision, and F1-score. Emon et al [44] have suggested cricket video summarization using deep learning. The video was divided into shots using Kernel temporal segmentation, which detects shot boundaries when they appear different in visual features.…”

Section: Introductionmentioning

confidence: 99%

Multimodal video abstraction into a static document using deep learning

Abdulsahib

Abdulmunim

2023

IJECE

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<p><span lang="EN-US">Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments.</span></p>

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Section: Introductionmentioning

confidence: 99%