A specific action pose recognition of hierarchical dance based on pose feature matching

To enhance the traditional method's limitations of low accuracy and prolonged feature matching times for specific dance action posture matching, we introduce a hierarchical approach for dance-specific action posture recognition. Initially, we utilise Kinect devices to capture real-time data and extract pertinent physical features. Subsequently, the K-means clustering algorithm is employed to extract keyframe features from the sequence, followed by image reconstruction using the active contour lasso method. Next, hierarchical dance movements are identified through two-dimensional manifold analysis, which enables us to derive the distribution function of edge contour features. Finally, the posture feature matching method is applied to align the functional outcomes, leading to recognition of specific action postures. Experimental results demonstrate that this method achieves a pose feature matching accuracy of 99.8% while reducing the matching time to 1.5 seconds. This method improves the performance of recognising specific movements and postures in graded dance.