Fine-grained action recognition of boxing punches from depth imagery

Abstract

Competitive sporting environments demand reliable statistics on an athlete’s performance to measure an athlete’s actions during competition, and to differentiate between the fine-grained actions performed. This is especially true for combat sports such as boxing where the variations observed between the main punching actions are subtle, making automatic classification of movements extremely difficult. This paper presents a robust framework for the automatic classification of a boxer’s punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through multi-class Support Vector Machine (SVM) and Random Forest classifiers using combinations of features. A coarse-to-fine hierarchical SVM classifier is presented in this paper based on prior knowledge of boxing punches. This framework has been applied to boxing image sequences taken at the Australian Institute of Sport with 14 elite boxers. Results demonstrate the effectiveness of the action recognition method, with the hierarchical SVM classifier yielding a 97.3% accuracy improving on the recent state-of-the-art action recognition systems.