Exploring machine learning techniques for penalty kick image classification: A performance analysis

This study examines the effectiveness of classical machine learning models in classifying penalty kick images extracted from video footage. The training and primary testing were based on frames captured from YouTube videos of penalty kicks. To evaluate the models' generalization ability, a separate test set was introduced, consisting of frames taken from self-recorded penalty kick videos filmed under real-world conditions. Seven widely used classifiers - Logistic Regression, Naive Bayes, Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) - were assessed using consistent preprocessing procedures. Among these, the Random Forest model delivered the best performance on both datasets, achieving an accuracy of 67.75% on the YouTube-based set and 63.33% on the self-filmed set. These findings demonstrate the robustness and cross-domain adaptability of ensemble learning methods, particularly Random Forest, in sports image classification tasks. The results provide a practical reference for developing lightweight and interpretable models in scenarios where deep learning may be less feasible.