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Motion Feature Retrieval in Basketball Match Video Based on Multisource Motion Feature Fusion

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  • Biao Ma
  • Minghui Ji

Abstract

Both the human body and its motion are three‐dimensional information, while the traditional feature description method of two‐person interaction based on RGB video has a low degree of discrimination due to the lack of depth information. According to the respective advantages and complementary characteristics of RGB video and depth video, a retrieval algorithm based on multisource motion feature fusion is proposed. Firstly, the algorithm uses the combination of spatiotemporal interest points and word bag model to represent the features of RGB video. Then, the directional gradient histogram is used to represent the feature of the depth video frame. The statistical features of key frames are introduced to represent the histogram features of depth video. Finally, the multifeature image fusion algorithm is used to fuse the two video features. The experimental results show that multisource feature fusion can greatly improve the retrieval accuracy of motion features.

Suggested Citation

  • Biao Ma & Minghui Ji, 2022. "Motion Feature Retrieval in Basketball Match Video Based on Multisource Motion Feature Fusion," Advances in Mathematical Physics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jnlamp:v:2022:y:2022:i:1:n:9965764
    DOI: 10.1155/2022/9965764
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    References listed on IDEAS

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    1. Ibai Errekagorri & Julen Castellano & Ibon Echeazarra & Carlos Lago-Peñas, 2020. "The effects of the Video Assistant Referee system (VAR) on the playing time, technical-tactical and physical performance in elite soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 20(5), pages 808-817, September.
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