Human Action Recognition Based on Fusion Features

Human action recognition has a wide range of application prospects in areas such as artificial intelligence and human-computer interaction. Action feature models and action recognition models are the basis of human action recognition. Based on the simplification of human skeleton model, the complementary features information such as the main joint angle, speed and relative position of the human body joint are extracted and fused to describe the behavioral gestures. And the action is expressed with the gesture series. A behavioral action model is established. In order to facilitate calculating, Fourier interpolation is performed on each action sample in the action database which taking the most characteristic dimension of the action video as the standard to keep the action samples feature dimensions consistent and normalized. And the principal components are used to extracting the main components of the feature, reducing the feature dimensions and redundant information. A one-to-many multi-category action recognition model was established based on the theory of support vector machines. The action recognition experiment was carried out with the open human action video database. The results showed that the algorithm has good adaptability and practicality.