Abstract: The complexity of human body movements, within-class differences and perspective changes hinder the accurate recognition of human movements. Depth camera can simultaneously record depth images and extract skeleton information; basing on that, a motion recognition method is proposed. Depth image sequence is used to generate a motion history point cloud and extract global features and local motion features from 3D skeleton information. Point cloud features and skeleton features are fused to construct a multi-modal feature fusion action recognition method. Test on two data sets of MSR-Action3D and UTKinect-Action3D shows that recognition rates are above 95% and 98.0%, which proves the effectiveness of the method

Key words: action recognition, depth maps, skeleton information, feature fusion