Goal Recognition in Soccer Matches using Deep Learning Techniques

Goal recognition in football matches is essential for several uses, including video summary, live event tracking, and sports analytics. Manual annotation or established criteria are common in traditional systems; however, they could not be scalable or robust enough. Here, the module provides a new method for goal recognition in football games that uses deep learning algorithms, namely GRUs and Convolutional Neural Networks (CNNs). Instead of relying on rule-based systems or time-consuming human labelling, our approach seeks to identify and categories target events autonomously from unprocessed video material. Spatiotemporal elements from football match footage can be efficiently extracted using a CNN-GRU architecture. To mimic temporal dynamics, the GRU component learns the sequence of frames over time, whereas the CNN component extracts spatial information from video frames. Proposed work introduces the CNN and GRU networks to train our model to recognize complicated patterns associated with goals in football matches. To perform experiments using publicly available football match datasets, encompassing both professional and amateur-level games, to assess the efficacy of our suggested methodology. In goal recognition tasks, our CNN-GRU strategy outperforms baseline methods and state-of-the-art techniques, according to the experimental data. In addition, the presented work uses visualization approaches to highlight the learned representations of goal-related characteristics and how interpretable our model’s predictions are.