A Cost Effective Method for Matching the 3D Motion Trajectories

Abstract

3D trajectory data have progressively become common since more devices which are possible to acquire motion data were produced. These technology advancements promote studies of motion analysis based on the 3D trajectory data. Even though similarity measurement of trajectories is one of the most important tasks in 3D motion analysis, existing methods are still limited. Recent researches focus on the full length 3D trajectory data set. However, it is not true that every point on the trajectory plays the same role and has the same meaning. In this situation, we developed a new cost effective method that uses the feature ‘signature’ which is a flexible descriptor computed only from the region of ‘elbow points’. Therefore, our proposed method runs faster than other methods which use the full length trajectory information. The similarity of trajectories is measured based on the signature using an alignment method such as dynamic time warping (DTW), continuous dynamic time warping (CDTW) or longest common subsequence (LCSS) method. In the experimental studies, we compared our method with two other methods using Australian sign word dataset to demonstrate the effectiveness of our algorithm.