Enterprise AI Analysis
SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization
This paper introduces SoccerHigh, a novel curated dataset and benchmark for automatic soccer video summarization. Addressing the critical lack of publicly available datasets, SoccerHigh comprises 237 paired full-match and summary videos from major European leagues, complete with shot boundaries and reflecting diverse editorial styles. We also propose a baseline model achieving an F1 score of 0.3956 and a new objective evaluation metric constrained by ground-truth summary length, setting a new standard for research in this field.
Executive Impact: Bridging Research with Real-World Applications
The SoccerHigh benchmark significantly advances the field of automatic soccer video summarization, offering a robust foundation for enterprise-level applications in sports media and analytics. Key metrics demonstrate the immediate potential for enhanced efficiency and content generation.
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Curated Matches
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Baseline F1 Score
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Annotation Recall (0.45 IoU)
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Segments Captured (0.05 IoU)
Deep Analysis & Enterprise Applications
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SoccerHigh Dataset
Baseline Model
Ablation Studies & Metrics
Introducing SoccerHigh: A Curated Dataset for Robust Summarization
The SoccerHigh dataset addresses the critical need for publicly available, annotated datasets in soccer video summarization. It features 237 paired full-match and summary videos from Spanish, French, and Italian leagues, using broadcast footage aligned at the shot level. This rich dataset supports the development of models that capture the unique dynamics and diverse editorial styles of soccer highlights.
Semi-Automated Annotation Pipeline
Detect Summary Shot Boundaries
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Extract Frame Features (DINOv2)
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Align Summary Shots to Broadcast Segments
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Manually Refine Correspondences
| SBD | Backbone | Precision | Recall | F1 Score | IoU |
|---|---|---|---|---|---|
| KNN | DINOv2 | 0.8578 | 0.8244 | 0.8407 | 0.7252 |
| TransNet (0.05 th) | DINOv2 | 0.7872 | 0.7373 | 0.7615 | 0.6148 |
The DINOv2 backbone with a kNN-based segmentation approach significantly outperforms others, achieving a high F1 score of 0.8407 for aligning summary shots to broadcast video. This approach minimizes manual annotation effort by providing strong initial alignment cues.
| IoU Threshold | Precision | Recall |
|---|---|---|
| 0.05 | 0.7649 | 0.9256 |
| 0.45 | 0.5949 | 0.7174 |
| 0.95 | 0.1567 | 0.1763 |
| Time Tolerance (s) | Precision | Recall |
|---|---|---|
| 0 | 0.7709 | 0.9258 |
| 60 | 0.8687 | 0.9857 |
| 90 | 0.8817 | 0.9896 |
The annotation process achieves a high recall, indicating that over 70% of segments achieve at least 0.45 IoU, and nearly all (98.57%) can be recovered within a 1-minute time tolerance, significantly reducing manual effort.
SoccerHigh Baseline Model: Foundation for Future Research
We introduce a robust baseline model for automatic soccer video summarization. This architecture comprises feature extraction, a Transformer encoder, and a classification head, processing fixed-length video chunks to identify key moments. The model is trained to select important shots, serving as a reference point for future advancements.
Baseline Model Architecture Overview
Input Broadcast Chunks
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Feature Extraction (VideoMAEv2 Giant)
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Transformer Encoder
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Prediction Head (Classification & Regression)
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Key Shot Selection (NMS)
| Backbone | Precision | Recall | F1 Score | # Params |
|---|---|---|---|---|
| VideoMAEv2 - ViT giant | 0.4796 | 0.3367 | 0.3956 | 1011.6M |
| VideoMAEv2 - ViT small | 0.4426 | 0.2797 | 0.3428 | 21.9M |
| CLIP | 0.3603 | 0.2335 | 0.2797 | 151.3M |
VideoMAEv2 (ViT giant) shows superior performance (F1: 0.3956) due to its ability to capture both spatial and temporal dependencies, highlighting the importance of video-specific feature extraction.
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Baseline F1 Score on Test Set
The proposed baseline achieves an F1 Score of 0.3956 on the test set, setting a benchmark for future research in soccer video summarization.
Optimizing the Baseline: Ablation Insights
Extensive ablation studies were conducted to identify optimal configurations for the baseline model. We evaluated the impact of different chunk sizes, prediction heads, and data augmentation strategies to maximize performance and generalization.
| Chunk Size (s) | Precision | Recall | F1 Score |
|---|---|---|---|
| 15 | 0.4619 | 0.2946 | 0.3598 |
| 60 | 0.4796 | 0.3367 | 0.3956 |
| 120 | 0.4650 | 0.3114 | 0.3730 |
A chunk length of 60 seconds yields the optimal F1 score (0.3956), balancing local and contextual information for effective highlight identification.
| Head | NMS | Precision | Recall | F1 Score |
|---|---|---|---|---|
| Classification | X | 0.6450 | 0.2395 | 0.3493 |
| Classification + Regression | X | 0.6041 | 0.2679 | 0.3712 |
| Classification + Regression | ✓ | 0.4796 | 0.3367 | 0.3956 |
Combining classification and regression heads with Non-Maximum Suppression (NMS) significantly boosts F1 score to 0.3956, demonstrating its crucial role in refining shot proposals.
| MixUp | Precision | Recall | F1 Score |
|---|---|---|---|
| X | 0.4421 | 0.3115 | 0.3655 |
| ✓ | 0.4796 | 0.3367 | 0.3956 |
MixUp data augmentation improves generalization, leading to an F1 score of 0.3956 compared to 0.3655 without it.
Objective Evaluation with F1 Score@T
We introduce a new objective evaluation metric, F1 Score@T, which constrains the predicted summary length to match the ground truth. This approach allows for a more objective assessment by focusing on the model's ability to identify key events without imposing rigid length constraints. The baseline model achieves an F1 Score@T of 0.3883, setting a more flexible and objective benchmark for future research.
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