  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.
