Abstract
With the development of computer vision and artificial intelligence technologies, the “Intelligent Arena” is becoming one of the new-emerging applications and research topics. Different from conventional sports video highlight detection, the intelligent playground can supply real-time and automatic sport video broadcast, highlight video generation, and sport technological analysis. In this paper, we have proposed a deep learning based intelligent basketball arena system to automatically broadcast the basketball match. First of all, with multiple cameras around the playground, the proposed system can automatically select the best camera to supply real-time high-quality broadcast. Furthermore, with basketball energy image and deep conventional neural network, we can accurately capture the scoring clips as the highlight video clips to supply the wonderful actions replay and online sharing. Finally, evaluations on a built real-world basketball match dataset demonstrate that the proposed system can obtain 94.59% accuracy with only 45 ms processing time (i.e., 10 ms live camera selection, 30 ms hotspot area detection, and 5 ms BEI+CNN) for each frame. As the outstanding performance, the proposed system has already been integrated into the commercial intelligent basketball arena applications.
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Notes
- 1.
“Intelligent Arena,” http://www.huiti.com.
- 2.
“Beikantai,” http://www.kantai.tv.
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Acknowledgement
This work is partially supported by the National High Technology Research and Development Program of China (2014AA015101), the National Natural Science Foundation of China (No. 61602049), the Funds for Creative Research Groups of China (No. 61421061), the Beijing Training Project for the Leading Talents in S&T (ljrc 201502), and the Fundamental Research Funds for the Central University (No. 2016RC43).
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Liu, W., Liu, J., Gu, X., Liu, K., Dai, X., Ma, H. (2017). Deep Learning Based Intelligent Basketball Arena with Energy Image. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_49
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