A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence
Publishing highlights after a sports game is a common practice in the broadcast industry, providing viewers with a quick summary of the game and highlighting interesting events. However, the manual process of compiling all the clips into a single video can be time-consuming and cumbersome for video...
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Ediciones Universidad de Salamanca
2024-12-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31615 |
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| author | Arjun Sivaraman Tarun Kannuchamy Anmol Anand Shivam Dheer Devansh Mishra Narayanan Prasanth S. P. Raja |
| author_facet | Arjun Sivaraman Tarun Kannuchamy Anmol Anand Shivam Dheer Devansh Mishra Narayanan Prasanth S. P. Raja |
| author_sort | Arjun Sivaraman |
| collection | DOAJ |
| description | Publishing highlights after a sports game is a common practice in the broadcast industry, providing viewers with a quick summary of the game and highlighting interesting events. However, the manual process of compiling all the clips into a single video can be time-consuming and cumbersome for video editors. Therefore, the development of an artificial intelligence (AI) model for sports highlight generation would significantly reduce the time and effort required to create these videos and improve the overall efficiency and accuracy of the process. This would benefit not only the broadcast industry but also sports fans who are looking for a quick and engaging way to catch up on the latest games. The objective of the paper is to develop an AI model that automates the process of sports highlight generation by taking a match video as input and returning the highlights of the game. The approach involves creating a list of words (wordnet) that indicate a highlight and comparing it with the commentary audio’s transcript to find a similarity, making use of a speech-to-text conversion, followed by some pre-processing of the extracted text, vectorization and finally measurement of the cosine similarity metric between the text and the wordnet. However, this process can become time-consuming too, in case of longer match videos, as the computation times of the AI models become inefficient. So, we used a parallel processing technique to counter the time required by the AI models to compute the outputs on large match videos, which can decrease the overall time complexity and increase the overall throughput of the model. |
| format | Article |
| id | doaj-art-edfb80d45be04cd980ee08120fc771d3 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-edfb80d45be04cd980ee08120fc771d32025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-12-0113e31615e3161510.14201/adcaij.3161537096A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial IntelligenceArjun Sivaraman0Tarun Kannuchamy1Anmol Anand2Shivam Dheer3Devansh Mishra4Narayanan Prasanth5S. P. Raja6School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014Publishing highlights after a sports game is a common practice in the broadcast industry, providing viewers with a quick summary of the game and highlighting interesting events. However, the manual process of compiling all the clips into a single video can be time-consuming and cumbersome for video editors. Therefore, the development of an artificial intelligence (AI) model for sports highlight generation would significantly reduce the time and effort required to create these videos and improve the overall efficiency and accuracy of the process. This would benefit not only the broadcast industry but also sports fans who are looking for a quick and engaging way to catch up on the latest games. The objective of the paper is to develop an AI model that automates the process of sports highlight generation by taking a match video as input and returning the highlights of the game. The approach involves creating a list of words (wordnet) that indicate a highlight and comparing it with the commentary audio’s transcript to find a similarity, making use of a speech-to-text conversion, followed by some pre-processing of the extracted text, vectorization and finally measurement of the cosine similarity metric between the text and the wordnet. However, this process can become time-consuming too, in case of longer match videos, as the computation times of the AI models become inefficient. So, we used a parallel processing technique to counter the time required by the AI models to compute the outputs on large match videos, which can decrease the overall time complexity and increase the overall throughput of the model.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31615parallel computinghighlights generationmultiprocessingspeech to textartificial intelligencenatural language processingcosine similarity |
| spellingShingle | Arjun Sivaraman Tarun Kannuchamy Anmol Anand Shivam Dheer Devansh Mishra Narayanan Prasanth S. P. Raja A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence Advances in Distributed Computing and Artificial Intelligence Journal parallel computing highlights generation multiprocessing speech to text artificial intelligence natural language processing cosine similarity |
| title | A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence |
| title_full | A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence |
| title_fullStr | A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence |
| title_full_unstemmed | A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence |
| title_short | A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence |
| title_sort | parallel approach to generate sports highlights from match videos using artificial intelligence |
| topic | parallel computing highlights generation multiprocessing speech to text artificial intelligence natural language processing cosine similarity |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31615 |
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