Dynamic time warping in classification and selection of motion capture data

The paper is a comprehensive study on classification of motion capture data on the basis of dynamic time warping (DTW) transform. It presents both theoretical description of all applied and newly proposed methods and experimentally obtained results on real dataset of human gait with 436 samples of 30 males. The recognition is carried out by the classical DTW nearest neighbors classifier and introduced DTW minimum distance scheme. Class prototypes are determined on the basis of DTW alignment and chosen methods of averaging rotations represented by Euler angles and unit quaternions. In the basic classification approach the whole pose configuration space is taken into account. The influence of different rotation distance functions operating on Euler angles and unit quaternions, on an obtained accuracy of recognition is investigated. What is more, a differential filtering in time domain which approximates angular velocities and accelerations of subsequent joints is utilized. Because in the case of unit quaternions representing rotations classical subtraction is unworkable, the differential filtering based on a product with a conjugated quaternion is applied. The main contribution of the paper is also related to the proposed and successfully validated approach of an exploration of pose configuration space. It selects the most discriminative joints of a skeleton model in considered classification problem in a binary or fuzzy way. The introduced approach utilizes hill climbing and genetic search strategies as well as DTW transform based evaluation. The selection makes the recognition more efficient and reduces pose signatures.