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This project offers a deep studying approach to trendy lengthy soccer films the use of the spatiotemporal gaining knowledge of abilities of a three-D Convolutional Neural Network (three-D-CNN) and a Long Short Memory (LSTM)-Recurrent Neural Network (RNN). The method of our assignment consists of: 1) stepwise development of a Residual Network (ResNet) based totally on 3-d-Cnn, which recognizes football activities, 2) manual annotation of 744 soccer clips from five instructions of football pastime to shape and 3) LSTM Network education on soccer functions extracted the use of the goals 3-d-Cnn is based totally on ResNet. We combine 3-D-CN and LSTM models to determine the quality soccer moments. To summarise, a video footer triggers a version of the enter video signal as a series of video segments, whose inclusion in the composite video manufacturing is based totally on its demonstrated relevance. To examine the proposed aggregation system, 10 football motion pictures have been aggregated and then viewed with the aid of 48 members from eight countries and scored using a mean score (MOS) scale. Collectively, the linked motion pictures are rated 4 out of 5 MOS.
"SPORTS MATCH VIDEO TO TEXT SUMMARIZATION USING NEURAL NETWORK", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.71 - 75, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305007.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator