Detecting Content Segments from Online Sports Streaming Events: Challenges and Solutions

• Main Authors: Liu, Zongyi, Feng, Yarong, Luo, Shunyan, Ling, Yuan, Dong, Shujing, Wang, Shuyi
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Applications; Artificial neural networks; Computer vision; Games; Machine learning algorithms; Quality assessment; Smartphones / end user devices; Streaming media; Telecommunication traffic; Video recognition and understanding; Vision + language and/or other modalities
• ISSN: 2642-9381
• DOI: 10.1109/WACV57701.2024.00629

Developing a client-side segmentation algorithm for on-line sports streaming holds significant importance. For instance, in order to assess the video quality from an end-user perspective such as artifact detection, it is important to initially segment the content within the streaming playback. The challenge lies in localizing the content due to the intricate scene changes between content and non-content sections in popular sports like football, tennis, baseball, and more. Client-side content detection can be implemented in two ways: intrusively, involving the interception of network traffic and parsing service provider data and logs, or non-intrusively, which entails capturing streamed videos from content providers and subjecting them to analysis using computer vision technologies. In this paper, we introduce a non-intrusive framework that leverages a combination of traditional machine learning algorithms and deep neural networks (DNN) to distinguish content sections from noncontent sections across various online sports streaming services. Our algorithm has demonstrated a remarkable level of accuracy and effectiveness in sports broadcasting events, effectively overcoming the complexities introduced by intricate non-content insertion methods during the games.