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ViewAdapt-Det: view-adaptive detection for soccer broadcasting videos.
Chunwang Zhu1, Yongli Zhang2, Sheng Gao1
1Mudanjiang Normal University, Mudanjiang, 157011, China.
Scientific Reports
|June 22, 2026
Summary
This study introduces ViewAdapt-Det, a novel framework for soccer object detection that tackles challenges from frequent view switching. It significantly improves detection accuracy and robustness, outperforming existing methods in dynamic broadcast scenarios.
Area of Science:
- Computer Vision
- Machine Learning
- Sports Analytics
Background:
- Object detection in soccer videos is crucial for broadcasting and analysis.
- Frequent view switching in broadcasts creates challenges like abrupt visual changes and varying object appearances.
- Existing methods struggle with cross-view noise and diverse object scales.
Purpose of the Study:
- To develop a robust object detection framework for soccer videos that addresses view switching challenges.
- To improve detection accuracy and robustness in dynamic broadcast environments.
- To handle variations in object appearance across different camera views.
Main Methods:
- Proposed ViewAdapt-Det framework with two modules: View-Aware Temporal Gating (VATG) and View-Conditioned Detection Modulation (VCDM).
- VATG dynamically controls temporal aggregation based on shot transitions, suppressing cross-view noise.
- VCDM modulates detection features based on inferred view type for view-specific processing.
Main Results:
- ViewAdapt-Det demonstrated superior performance compared to existing methods on SoccerNet-Tracking, SportsMOT, and BroadcastSwitch-Soccer datasets.
- The framework showed enhanced robustness against abrupt shot transitions.
- VATG effectively managed temporal aggregation during view changes, and VCDM adapted feature processing to different views.
Conclusions:
- ViewAdapt-Det offers a robust solution for object detection in soccer videos with frequent view changes.
- The proposed modules effectively mitigate challenges posed by temporal and feature dimension variations.
- This framework advances intelligent broadcasting, tactical analysis, and player tracking in soccer.
