A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence
Abstract 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.
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Wu, L. (2022). Research on the Development and Application of Parallel Programming Technology in Heterogeneous Systems. Journal of Physics: Conference Series. 2173(1), 012042. 10.1088/1742-6596/2173/1/012042
Aziz, A. Z., Abdulqader, D. N., Sallow, A. B., & Omer, H. K. (2021). Python Parallel Processing and Multiprocessing: A Review. Academic Journal of Nawroz University. 10(3), 345–354. 10.25007/ajnu.v10n3a1145
Gagliardi, G., Gregori, L., & Ravelli, A. A. (2020). An NLP Pipeline as Assisted Transcription Tool for Speech Therapists. 12th International Conference on Language Resources and Evaluation (LREC 2020), 124-130.
Gunawan, D., Sembiring, C. A., & Budiman, M. A. (2018). The Implementation of Cosine Similarity to Calculate Text Relevance between Two Documents. Journal of physics: Conference Series, 978(1), 012120. 10.1088/1742-6596/978/1/012120
Iqbal, M., Abid, M. M., Khalid, M. N., & Manzoor, A. (2020). Review of feature selection methods for text classification. International Journal of Advanced Computer Research, 10(49), 138-152. 10.19101/IJACR.2020.1048037
Islam, M. R., Paul, M., Antolovich, M., & Kabir, A. (2019). Sports Highlights Generation using Decomposed Audio Information. 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China, 2019. 579-584. 10.1109/ICMEW.2019.00105
Malik, M., Malhotra, S., & Prasanth, N. (2020). Time Improvement of Smith-Waterman Algorithm Using OpenMP and SIMD. Communications in Computer and Information Science. 1206(CCIS), 686–697. 10.1007/978-981-15-4451-4_54
Midhu, K., & Padmanabhan, N. K. A. (2018). Highlight generation of cricket match using deep learning. In Lecture Notes in Computational Vision and Biomechanics (Vol. 28, pp. 925-936). Springer, Cham. 10.1007/978-3-319-71767-8_79
Naik, B.T., Hashmi, M.F., & Bokde, N.D. (2022). A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions. Applied Sciences, 12(9), 4429. 10.3390/app12094429
Paliwal, M., Chilla, R., Prasanth, N., Goundar S., & Raja S.P. (2022). Parallel implementation of solving linear equations using OpenMP. International Journal of Information Technology, 14(2), 1677–1687. 10.1007/s41870-022-00899-9
Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022). Robust Speech Recognition via Large-Scale Weak Supervision. 40th International Conference on Machine Learning, PMLR, 202, 28492-28518.
Shukla, P. et al. (2018). Automatic Cricket Highlight Generation Using Event-Driven and Excitement-Based Features. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 2018. 1881-18818. 10.1109/CVPRW.2018.00233
Thompson, V. U., Panchev, C., & Oakes, M. (2015). Performance Evaluation of Similarity Measures on Similar and Dissimilar Text Retrieval. 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), 1, 577-584. 10.5220/0005619105770584
Trivedi, A., Pant, N., Shah, P., Sonik, S., & Agrawal, S. (2018). Speech to text and text to speech recognition systems - A Review. IOSR Journal of Computer Engineering, 20(2), 36-43.
Wei, J., & Zou, K. (2019). EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, 6382–6388. 10.18653/v1/D19-1670
Wu, L. (2022). Research on the Development and Application of Parallel Programming Technology in Heterogeneous Systems. Journal of Physics: Conference Series. 2173(1), 012042. 10.1088/1742-6596/2173/1/012042
Sivaraman, A., Kannuchamy, T., Anand, A., Dheer, S., Mishra, D., Prasanth, N., & Raja, S. P. (2024). A Parallel Approach to Generate Sports Highlights from Match Videos Using Artificial Intelligence. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31615. https://doi.org/10.14201/adcaij.31615
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