DTW-Curve for classification of logically similar motions

Logical classification of motion data is the precondition of motion editing and behaviour recognition. The typical distance metrics of sequences can not identify logical relation between motions well. Based on the traditional DTW distance metrics, this paper proposes strategies bidirectional DTW and segment DTW, both of which could improve the robustness of identifying logically related motions, and then proposes a DTW-Curve method which is used to compare the logical similarity between the motions. The generation of DTW-Curve includes three steps. Firstly, motions should be normalized to remove the global translation and align the global orientation. Secondly, motions are resampled to cluster local frames and remove redundant frames. Finally, DTW-Curve is generated under the control of different thresholds. DTW-Curve may produce many statistical properties, which could be used to unsupervised logical classification of motions. We propose two types of statistical properties, and classify motion data by using hierarchical clustering procedure. The experiment results demonstrate that the logical classification based on DTW-Curve has better classification performance and robustness.