This project uses computer vision, natural language processing and audio analysis to automatize the highlights generation task for tennis matches. Computer vision techniques such as camera shot detection, hough transform and neural networks are used to extract the time intervals of the points. To detect the best points, three approaches are used. Point length suggests which points correspond to rallies and aces. The audio waves are analyzed to search for the highest audio peaks, which indicate the moments where the crowd cheers the most. Sentiment analysis, a natural language processing technique, is used to look for points where the commentators make positive statements. The software receives a full tennis match with no cuts as the input, and outputs a short summary with the most relevant points. The final software pipeline was tested on three tennis matches from the 2021 US Open for manual validation.
Computer Science; Mathematics
Liberman, Alon, "Highlights Generation For Tennis Matches Using Computer Vision, Natural Language Processing And Audio Analysis" (2022). Senior Independent Study Theses. Paper 9837.
Artificial Intelligence and Robotics | Other Mathematics
Bachelor of Arts
Senior Independent Study Thesis Exemplar
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