A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence

• Published in: Advances in distributed computing and artificial intelligence journal Vol. 13; p. e31615
• Main Authors: Sivaraman, Arjun, Kannuchamy, Tarun, Anand, Anmol, Dheer, Shivam, Mishra, Devansh, Prasanth, Narayanan, Raja, S. P.
• Format: Journal Article
• Language: English
• Published: Salamanca Ediciones Universidad de Salamanca 01.01.2024
• Subjects: Accuracy; Artificial intelligence; Automation; Broadcasting; cosine similarity; Distributed processing; Efficiency; Games; highlights generation; multiprocessing; Natural language processing; parallel computing; Parallel processing; Similarity; speech to text; Sports; Support vector machines; Video
• ISSN: 2255-2863; 2255-2863
• DOI: 10.14201/adcaij.31615

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.