Detection and localization of scorebox in long duration broadcast sports videos

Abdullah Aman Khan; Haoyang Lin; Saifullah Tumrani; Zheng Wang; Jie Shao

doi:10.1117/12.2575834

12 October 2020 Detection and localization of scorebox in long duration broadcast sports videos

Abdullah Aman Khan, Haoyang Lin, Saifullah Tumrani, Zheng Wang, Jie Shao

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740J (2020) https://doi.org/10.1117/12.2575834
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

Many studies have been devoted to sports video summarization and content-based video search. However, the semantic importance of caption box or scorebox (SB) appearing in broadcast sports videos has been almost neglected as SB holds key elements for conducting these research tasks. SB localization is challenging as there exists a huge variety of SBs, and almost every broadcast sports video contains a different SB with unique features such as geometry, font, colors, location, and texture. Every time a new sports series emerges, it contains a new type of scorebox that never resembles any other sports series. One can say that, SBs are evolving with unexpected features and novel challenges. Thus, traditional learning-based methods alone are not suitable for detection. This paper proposes a robust method for detecting and localizing SBs appearing in broadcast sports videos. It automatically learns the template of SB and further utilizes the template, as the SB may translate from the usual location and may disappear for a short time. We performed comprehensive experiments on a real-life dataset SP-1 and comparison with state-of-the-art methods shows that the proposed method achieves better performance.

Citation Download Citation

Abdullah Aman Khan, Haoyang Lin, Saifullah Tumrani, Zheng Wang, and Jie Shao "Detection and localization of scorebox in long duration broadcast sports videos", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740J (12 October 2020); https://doi.org/10.1117/12.2575834

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