Sports Activity Recognition - Shot Put, Discus, Hammer and Javelin Throw

This paper describes the classification of sports activities using still images. Sports activities specifically include discus throw, shot put and javelin throw. This system is useful for athletes preparing for these sports and it will help them to understand and practice the right posture for these sports. Along with them, this model is also helpful to the coaches to train their players and increase their chances of winning. Dataset is created by collecting the different images of the following sports from different sources. Various machine learning algorithms such as random forest, KNN, SVM (Polynomial), voting classifier has been applied to the model out of which SVM (poly) performed better than the rest with an accuracy of 98.7%. Voting classifier and Random Forest has also performed well. BRISK feature descriptor method is found useful to get point of interest in the image dataset. Techniques like image augmentation used for creating artificial dataset to which is useful for training the machine learning model and Principal Component Analysis (PCA) used for dimensionality reduction is found helpful to increase the accuracy of the model.