Advancing Football Game Analysis: Integrating Computer Vision, Deep Learning, and Hybrid Techniques for Enhanced Video Analytics

This research is about football analytics with the help of deep learning and computer vision, through which we can track the movement of the ball and all ball possessions a particular team has. This will assist teams in properly analyzing their game and identifying areas where they may be lacking. The fusion of YOLO which is one of the most popular model architectures and object detection algorithm and deep learning techniques like 3DCNN, CNN, Faster R-CNN, LSTM, BLSTM and computer vision has revolutionized the landscape of sports analytics, particularly in the domain of football (soccer). This abstract provides a glimpse into the exciting field of football analytics through the lens of advanced technologies. In recent years, the application of deep learning models to football data has enabled the extraction of invaluable insight from the game. This abstract serves as a gateway to the exciting world of football analytics, where deep learning and computer vision come together to revolutionize the way we perceive and engage with the beautiful game. The possibilities are limitless, as ongoing research continues to refine models and algorithms, promising even deeper insights and a richer experience for football enthusiasts worldwide.