Hockey activity recognition using pre-trained deep learning model
Keerthana Rangasamy, Muhammad Amir As’ari, Nur Azmina Rahmad, Nurul Fathiah Ghazali
Abstract
Activity recognition in sports is often complex task resulting from the rapid dynamic interaction within players. In this paper, pre-trained VGG-16, deep learning based hockey activity recognition model has been proposed. Own hockey dataset consisting of four main activity includes free hit, goal, penalty corner and long corner was constructed as there are no existing field hockey datasets available. Experimental results indicate that the pre-trained deep learning model generates comparative results on this challenging dataset by tweaking the hyperparameters of this pre-trained model.
Topics & Concepts
TweakingField hockeyArtificial intelligenceComputer scienceDeep learningActivity recognitionMachine learningHyperparameterTask (project management)Pattern recognition (psychology)EngineeringFootballOperating systemPolitical scienceLawSystems engineeringAnomaly Detection Techniques and ApplicationsHuman Pose and Action RecognitionVideo Analysis and Summarization