Qualitative Motion of Point-like Objects

In this paper, we introduce a framework to represent the course of motion of point-like objects qualitatively, i.e. by making only as many distinctions as necessary in a given context. The need to describe the course of motion arises in many applications, such as robotics and motion planning, automatic control and surveillance systems, image sequence analysis and indexing, and animation. We use a bottom-up approach that starts from basic descriptional units for orientation, length and velocity?integrated in the concept of qualitative motion vectors (QMVs)?and the operations on them. In order to describe sequences of QMVs we explore qualitative means of obtaining ordering information and of classifying significant turns. We then move on to issues of generalization discerning global, local and `turn-based? variants. Finally, we develop techniques for identifying higher-level motion patterns such as closed loops and spirals.