Ensemble based 3D human motion classification

Due to the rapid development of motion capture technology, more and more human motion databases appear. In order to effectively and efficiently manage human motion database, human motion classification is necessary. In this paper, we propose an ensemble based human motion classification approach (EHMCA). Specifically, EHMCA first extracts the descriptors from human motion sequences. Then, singular value decomposition (SVD) is adopted to reduce the dimensionality of all the feature vectors. In the following step, a cluster ensemble approach is designed to construct the consensus matrix from the feature vectors. Finally, the normalized cut algorithm is applied to partition the consensus matrix and assign the feature vectors into the corresponding clusters. Experiments on the CMU database illustrate that the proposed approach achieves good performance.