Human activity recognition based on 3D skeletons and MCRF model

Considering the disadvantages of the traditional human activity recognition system, a human activity recognition system using an MCRF model and 3D skeletons was proposed. Its 3D skeleton data has less data and retains the key information, and the MCRF model has the advantage of being able to combine more features and utilizing adaptive contextual information. First, human activity was divided into global activity, arm activity, and leg activity. Several feature subsets were formed through more feature extraction. Then, CRF models were used on each feature subset to generate CRF units. Finally, all the CRF units were combined to produce the MCRF model which was utilized to recognize human activity. The experimental results indicate that the proposed method can improve detection accuracy.