Tracking perceptually indistinguishable objects using spatial reasoning

Abstract

Intelligent agents perceive the world mainly through images captured at different time points. Being able to track objects from one image to another is fundamental for understanding the changes of the world. Tracking becomes challenging when there are multiple perceptually indistinguishable objects (PIOs), i.e., objects that have the same appearance and cannot be visually distinguished. Then it is necessary to reidentify all PIOs whenever a new observation is made. In this paper we consider the case where changes of the world were caused by a single physical event and where matches between PIOs of subsequent observations must be consistent with the effects of the physical event. We present a solution to this problem based on qualitative spatial representation and reasoning. It can improve tracking accuracy significantly by qualitatively predicting possible motions of objects and discarding matches that violate spatial and physical constraints. We evaluate our solution in a real video gaming scenario.