Relational object tracking and learning

We propose a relational model for online object tracking during human activities using the Distributional Clauses Particle Filter framework, which allows to encode commonsense world knowledge such as qualitative physical laws, object properties as well as relations between them. We tested the framework during a packaging activity where many objects are invisible for longer periods of time. In addition, we extended the framework to learn the parameters online and tested it in a tracking scenario involving objects connected by strings.