Program Type
Graduate
Faculty Advisor
Dr. Robin Ghosh
Document Type
Presentation
Location
Face-to-face
Start Date
18-4-2024 11:00 AM
End Date
18-4-2024 11:30 AM
Abstract
The field of sports video analysis using deep learning is rapidly advancing. Proper classification and analysis of sports videos are essential to manage the growing sports media content. It offers numerous benefits for the media, advertising, analytics, and education sectors. Soccer, also known as football, worldwide, is among the most popular sports. This research study used a deep learning-based approach for soccer action detection. Deep learning has become a popular machine learning technique, especially for image and video classification. We have used the SoccerAct dataset, which consists of ten soccer actions like corner, foul, freekick, goal kick, long pass, on target, penalty, short pass, substitution, and throw-in. Our study analyzes ConvLSTM and LRCN, two different methods for soccer action detection in video clips. ConvLSTM is an extension of the Long Short-Term Memory (LSTM) architecture that allows the modeling of both spatial and temporal dependencies in sequential data by integrating convolutional operations into recurrent neural networks (RNNs).On the other hand, convolutional neural networks (CNNs) and recurrent units -usually LSTM or GRU-combine in Long-term Recurrent Convolutional Networks (LRCN) to capture temporal relationships through RNNs and spatial characteristics through CNNs. The ConvLSTM model achieved 70%, and the LRCN model achieved 71% accuracy on its first iteration. The model's performance is enhanced through fine-tuning the pre-trained model, incorporating batch normalization to mitigate overfitting, and conducting hyperparameter tuning. Our study highlights how deep learning techniques can improve soccer action detection systems significantly. A soccer action detection model could be used in various real-world contexts, including sports performance analysis, journalism and reporting, broadcasting, and many more.
Recommended Citation
Rahaman, Musfikur, "League of Learning: Deep Learning for Soccer Action Video Classification" (2024). ATU Research Symposium. 17.
https://orc.library.atu.edu/atu_rs/2024/2024/17
Included in
League of Learning: Deep Learning for Soccer Action Video Classification
Face-to-face
The field of sports video analysis using deep learning is rapidly advancing. Proper classification and analysis of sports videos are essential to manage the growing sports media content. It offers numerous benefits for the media, advertising, analytics, and education sectors. Soccer, also known as football, worldwide, is among the most popular sports. This research study used a deep learning-based approach for soccer action detection. Deep learning has become a popular machine learning technique, especially for image and video classification. We have used the SoccerAct dataset, which consists of ten soccer actions like corner, foul, freekick, goal kick, long pass, on target, penalty, short pass, substitution, and throw-in. Our study analyzes ConvLSTM and LRCN, two different methods for soccer action detection in video clips. ConvLSTM is an extension of the Long Short-Term Memory (LSTM) architecture that allows the modeling of both spatial and temporal dependencies in sequential data by integrating convolutional operations into recurrent neural networks (RNNs).On the other hand, convolutional neural networks (CNNs) and recurrent units -usually LSTM or GRU-combine in Long-term Recurrent Convolutional Networks (LRCN) to capture temporal relationships through RNNs and spatial characteristics through CNNs. The ConvLSTM model achieved 70%, and the LRCN model achieved 71% accuracy on its first iteration. The model's performance is enhanced through fine-tuning the pre-trained model, incorporating batch normalization to mitigate overfitting, and conducting hyperparameter tuning. Our study highlights how deep learning techniques can improve soccer action detection systems significantly. A soccer action detection model could be used in various real-world contexts, including sports performance analysis, journalism and reporting, broadcasting, and many more.