Multimodal Soccer Highlight Detection

A deep learning system for detecting highlight moments in soccer videos using multimodal data (vision, audio, and text).

Overview

This project combines three modalities to identify highlight events in soccer matches:

• Vision: Extracted video frames and spectrograms for visual features
• Audio: Mel-spectrograms from match audio commentary
• Text: Commentary text extracted from match broadcasts

Dataset

The dataset consists of 4,552 clips from soccer matches, split into:

• Train: 3,111 clips
• Validation: 664 clips
• Test: 778 clips

Each clip contains:

• Video frames (8-second clips from matches)
• Audio waveforms and mel-spectrograms
• Textual commentary
• Binary highlight/non-highlight labels

Requirements

• Python 3.8+
• PyTorch
• TorchAudio
• TorchVision
• NumPy, Pandas, Scikit-learn
• Matplotlib, Seaborn