A Review of Deep Learning Architectures for Automated Video Analysis in Football Events

Automating the process of analyzing the match footage is one of the important things that is around the football stakeholders. By using deep learning algorithms and architectures, we can have an advanced process of analysis. Here, we focus to delve into exploration of match analysis works in an automated manner. This architecture is covered with the aspects of player tracking, action recognition, semantic segmentation, and event detection using Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs), and 3D CNNs. Also, this work focuses on tactical analysis and performance evaluation, highlighting the usage of deep learning models for extracting insightful information from the footage. Integration of deep learning with traditional scouting methods is also discussed in this work with the implications for the field. Challenges that are faced during this process include data annotation, scalability, and real-time processing. Future research directions and suggestions are also discussed. The work offers a roadmap for researchers, practitioners, and stakeholders interested in deep learning architectures for sports video automation to advance sports technology and analytics.