A Systematic Review on Machine Learning-Based Sports Video Summarization Techniques

Vasudevan, Vani; Gounder, Mohan Sellappa

doi:10.1007/978-3-031-20541-5_1

Cited by 3 publications

(1 citation statement)

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“…This capability has numerous applications, from providing real-time highlights and replays for broadcasting purposes to aiding coaches and analysts in post-match analysis and strategic planning. Furthermore, automatic event detection technology can help sports organizations and broadcasters streamline their content production workflows, reducing the need for manual tagging and editing of footage [9]. This not only saves time and resources but also enables faster delivery of engaging and personalized content to fans.…”

Section: Introductionmentioning

confidence: 99%

Automatic Analysis and Event Detection Technology of Sports Competition Video Based on Deep Learning

Lixin Lai

2024

jes

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This paper presents an investigation into the application of advanced techniques, including deep learning classification and FCM centroid segmentation, for automated event detection and analysis in sports videos. Through a series of experiments and analyses, we demonstrate the effectiveness of deep learning models in accurately categorizing events within sports footage, alongside the insights provided by FCM segmentation into event occurrences at the frame level. This paper investigates the application of advanced techniques, such as deep learning classification and FCM centroid segmentation, for automated event detection in sports videos. Through experiments, we achieved an average accuracy of 93.2% using deep learning classification and identified key events with probabilities ranging from 0.01 to 0.90 using FCM segmentation.

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Section: Introductionmentioning

confidence: 99%