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Deep Learning-Based Football Player Detection in Videos.
1College of Physical Education, Qiqihar University, Qiqihar 161000, China.
Computational Intelligence and Neuroscience
|July 22, 2022
Summary
This study introduces a deep convolutional neural network algorithm for real-time football player detection. The method effectively tracks players, enhancing football video analysis and adaptable to other sports.
Area of Science:
- Computer Vision
- Sports Analytics
- Machine Learning
Background:
- Automated player detection and tracking are crucial for football video analysis.
- Existing methods may struggle with real-time performance and varying image quality.
Purpose of the Study:
- To develop a deep convolutional neural network (CNN) algorithm for real-time football player detection.
- To enhance the accuracy and adaptability of player detection models.
Main Methods:
- Utilized a CNN architecture with five convolution blocks for feature extraction.
- Implemented a feature fusion strategy combining multi-level features with weighted parameters.
- Designed the model for real-time processing and adaptability to diverse image resolutions and qualities.
Main Results:
- The proposed algorithm demonstrated effective real-time detection of football players.
- Feature fusion improved detection accuracy and robustness across different image conditions.
- The model showed potential for extension to player detection in other sports.
Conclusions:
- The deep convolutional neural network algorithm is effective for football player detection.
- The feature fusion technique enhances model performance and adaptability.
- This approach offers a versatile framework for sports video analysis.
