Efficient and Robust Motion Segmentation via Adaptive Similarity Metric

This paper introduces an efficient and robust method that segments long motion capture data into distinct behaviors. The method is unsupervised, and is fully automatic. We first apply spectral clustering on motion affinity matrix to get a rough segmentation. We combined two statistical filters to remove the noises and get a good initial guess on the cut points as well as on the number of segments. Then, we analyzed joint usage information within each rough segment and recomputed an adaptive affinity matrix for the motion. Applying spectral clustering again on this adaptive affinity matrix produced a robust and accurate segmentation compared with the ground-truth. The experiments showed that the proposed approach outperformed the available methods on the CMU Mocap database.