Football Event Classification Using Convolutional Autoencoder and Multilayer Extreme Learning Machine

Event detection and classification in sports are particularly important in the field of sports data analytics. In this era, various AI methods are utilized to detect events in a football (soccer) game. The use of advanced computational techniques in this area can help to achieve higher accuracy in detecting events. This letter proposes a novel architecture to classify events in a football game. The dataset, which is used, consists of numerous images pertaining to different events in a regular football match. From this dataset, the objective is to detect specific and semantically meaningful events, such as pass, dribble, or shoot. The proposed model consists of a convolutional autoencoder (AE) coupled with a multilayer extreme learning machine to classify the given images into defined events. The convolutional AE module is placed before the extreme learning machine (ELM) in the model pipeline as it helps with data compression and dimensionality reduction so as to ensure fast computation. Thereby, a multilayer extreme learning machine achieves better performance compared with the other existing approaches.