Title: Football highlights detection based on twin comparison algorithm and multimodal feature fusion

Authors: Xufeng Zhang

Addresses: College of Physical Education, Hunan City University, Yiyang, 413000, China

Abstract: To enhance the accuracy and robustness of football highlights detection (FHD), this study proposes an innovative method integrating Twin Comparison and multimodal features. It employs Twin Comparison for dual-threshold shot segmentation, classifying video scenes into four types: goals, corner kicks, penalty area attacks, and fouls. The approach uses a 3D convolutional neural network to extract spatiotemporal visual features and applies Mel-frequency cepstral coefficients for audio analysis. Multimodal information is integrated via post-fusion and classified using a Softmax classifier. Experiments demonstrated a shot segmentation precision of 92.4% and recall of 88.7%. The goal detection F1-score was 91.9%, while audio cheer detection reached 94.2%. The multimodal model achieved an average accuracy of 84.8%, an F1-score of 88.9%, and real-time processing at 33.5 FPS. The method significantly improves detection accuracy, effectively complements audiovisual features, and offers practical value for intelligent football video analysis, viewing experience, and content dissemination.

Keywords: 3DCNN; football match; dual threshold segmentation; multimodal feature fusion; MFCC.

DOI: 10.1504/IJICA.2026.154209

International Journal of Innovative Computing and Applications, 2026 Vol.15 No.5, pp.311 - 327

Received: 27 Aug 2025
Accepted: 03 Mar 2026
Published online: 16 Jun 2026 *

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