Article: Semantic event analysis of sports match videos using domain knowledge and deep features Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.47 pp.89 - 105 Abstract: Semantic event analysis in sports videos faces challenges such as complex actions and high annotation costs. To address these issues, this paper proposes a novel framework that integrates domain knowledge with deep features. The approach first translates sports rules into computable spatio-temporal constraints, then designs a knowledge-injection network to guide deep models toward semantically critical regions. Finally, a knowledge-conditioned attention mechanism is introduced to fuse domain knowledge with visual features effectively. Experimental results on the SoccerNet dataset demonstrate that the proposed method achieves a mean average precision of 71.5%, outperforming strong baselines such as inflated 3D ConvNet and soccer background matting network by 13.3% and 3.6%, respectively. The framework shows significant improvements in detecting complex and sparse events, offering enhanced accuracy, robustness and generalisation capability with reduced reliance on large-scale annotated data. Inderscience Publishers - linking academia, business and industry through research

Title: Semantic event analysis of sports match videos using domain knowledge and deep features

Authors: Ruixia Xu

Addresses: Sports Department, Henan University of Animal Husbandry and Economy, Zhengzhou, 450000, China

Abstract: Semantic event analysis in sports videos faces challenges such as complex actions and high annotation costs. To address these issues, this paper proposes a novel framework that integrates domain knowledge with deep features. The approach first translates sports rules into computable spatio-temporal constraints, then designs a knowledge-injection network to guide deep models toward semantically critical regions. Finally, a knowledge-conditioned attention mechanism is introduced to fuse domain knowledge with visual features effectively. Experimental results on the SoccerNet dataset demonstrate that the proposed method achieves a mean average precision of 71.5%, outperforming strong baselines such as inflated 3D ConvNet and soccer background matting network by 13.3% and 3.6%, respectively. The framework shows significant improvements in detecting complex and sparse events, offering enhanced accuracy, robustness and generalisation capability with reduced reliance on large-scale annotated data.

Keywords: semantic event analysis; domain knowledge; deep features; video understanding; sports videos.

DOI: 10.1504/IJICT.2025.150605

International Journal of Information and Communication Technology, 2025 Vol.26 No.47, pp.89 - 105

Received: 13 Sep 2025
Accepted: 16 Oct 2025
Published online: 17 Dec 2025 *

Title: Semantic event analysis of sports match videos using domain knowledge and deep features

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