A Framework for Evaluating Motion Segmentation Algorithms

TL;DR

This paper introduces a comprehensive framework with new evaluation methods and datasets for objectively comparing human motion segmentation algorithms across diverse use cases.

Contribution

It provides a unified testing ground, novel evaluation approach, and hierarchical labeling for motion segmentation algorithms, facilitating standardized comparison.

Findings

Framework enables consistent algorithm evaluation.

New datasets with hierarchical ground truth labels.

Integrated Kernel approach offers tailored quality measures.

Abstract

There have been many proposals for algorithms segmenting human whole-body motion in the literature. However, the wide range of use cases, datasets, and quality measures that were used for the evaluation render the comparison of algorithms challenging. In this paper, we introduce a framework that puts motion segmentation algorithms on a unified testing ground and provides a possibility to allow comparing them. The testing ground features both a set of quality measures known from the literature and a novel approach tailored to the evaluation of motion segmentation algorithms, termed Integrated Kernel approach. Datasets of motion recordings, provided with a ground truth, are included as well. They are labelled in a new way, which hierarchically organises the ground truth, to cover different use cases that segmentation algorithms can possess. The framework and datasets are publicly available and are intended to represent a service for the community regarding the comparison and evaluation of existing and new motion segmentation algorithms.