Motion Retrieval Based on Energy Morphing

Abstract

Matching and retrieval of motion sequences has become an important research area in recent years, due to the increasing availability and popularity of motion capture data. The main challenge in matching two motion sequences is the diversity of the captured motions, including variable length, local shifting, local and global scaling. Most existing methods employ Dynamic Time Warping (DTW) or Uniform Scaling to handle these problems. In this paper, we propose a novel content-based method for matching of this human motion captured data. We convert the matching problem of motion capture data into a transportation problem. To solve this problem efficiently, we employ Earth Mover's Distance (EMD) as the matching framework. To penalize any strayed matching, we provide a ground distance that works similar to SakoeChiba band of DTW. Empirical results obtained are encouraging.

Shape grammar extraction for efficient query-by-sketch pattern matching in long time series

“…The advantage of performing a locally adaptive approximation rather than a piecewise adaptive approximation (PAA) is that the latter can potentially smooth out important points in the data set due to the constant sized bins used to compute the average of all the time points within that bin. Smoothing of time series data by dimension reduction techniques such as the Douglas-Peucker (DP) algorithm [8] has been used in time series simplification methods [49,54]. While this is an effective method for time series smoothing, our algorithm operates on a symbolic approximation of the raw data and so avoids arithmetic calculation at each approximation step.…”

A Method of Hyper-sphere Cover in Multidimensional Space for Human Mocap Data Retrieval

“…Yu, et al, 2005), Ensemble HMM Learning based approach developed by Xiang and Zhu (J. Xiang and H. Zhu, 2007), energy morphing based method proposed by Tam et al (G. Tam, et al, 2007) and semantic matching based method (X.…”