Human action recognition using Histographic methods and hidden Markov models for visual martial arts applications
Human Action Recognition is being used with an increasing rate in applications designed to describe human activity in everyday life. However, some areas still remain far from the epicenter of scientific research, like dynamic problems of detection and classification of movements from visual Martial Arts. With this paper, we are proposing a novel recognition system focused on these types of action, based on the use of local spatio-temporal features from Histographic methods, as those extracted by Histograms of Oriented Gradient (HOG) and Histograms of Optical Flow (HOF), while we also pursue the reduction of the problem's dimensionality by applying on them Principal Components Analysis (PCA). In continuation, we combine these features with the use of Hidden Markov Models (HMM) in order to train models for each different movement. Our system is tested with very encouraging results upon a database comprising sequences of shotokan karate movements (katas), created by us for the needs of this research. In parallel, we additionally attach an educational character to our application, with the extraction of a score for the accuracy of execution of each movement based on the prototypes we have built.