Football Game Video Analysis Method with Deep Learning.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Sports Analytics

Background:

• Football video analysis is crucial due to the sport's popularity.
• Traditional methods for sports event detection lack granularity and event variety.
• Deep learning shows promise but has limited application in sports video event detection.

Purpose of the Study:

• To develop a deep learning model for precise football event detection in video content.
• To address the limitations of traditional machine learning in sports event analysis.
• To automatically locate and classify interesting event clips within long football game videos.

Main Methods:

• A two-stage deep learning approach was employed for event detection.
• The first stage uses a 3D Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network (Bi-RNN) to generate candidate event segments.
• The second stage refines predictions by filtering, merging, and classifying event fragments.

Main Results:

• The proposed deep learning model effectively detects and segments football events.
• The two-stage process successfully identifies event start and end positions.
• Experimental results validate the correctness and efficacy of the developed method.

Conclusions:

• Deep learning offers a powerful solution for detailed sports video event analysis.
• The developed model enhances the accuracy and scope of football event detection.
• This research contributes to advancing automated analysis of sports video content.