Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features

TL;DR

This paper introduces a novel semantic feature-based distance measure for clustering motion trajectories, improving clustering accuracy and significantly reducing runtime compared to traditional methods like dynamic time warping.

Contribution

A new flexible distance measure based on salient features for motion trajectory clustering, optimized for efficiency and applicability across different tasks.

Findings

Outperforms dynamic time warping in clustering accuracy.

Offers substantial runtime improvements, especially for long trajectories.

Effective on both robotic and human motion datasets.

Abstract

Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it allows automated analysis of recorded motion data. Many clustering algorithms for trajectories build upon distance metrics that are based on pointwise Euclidean distances. However, our work indicates that focusing on salient characteristics is often sufficient. We present a novel distance measure for motion plans consisting of state and control trajectories that is based on a compressed representation built from their main features. This approach allows a flexible choice of feature classes relevant to the respective task. The distance measure is used in agglomerative hierarchical clustering. We compare our method with the widely used dynamic time warping algorithm on test sets of motion plans for the Furuta pendulum and the Manutec robot arm and on real-world data from a human motion dataset. The proposed method demonstrates slight advantages in clustering and strong advantages in runtime, especially for long trajectories.