The explicit recognition of the relationships between interacting objects can improve the understanding of their dynamics. In this work, we investigate the use of Relational Dynamic Bayesian Networks to represent the interactions between moving objects in a surveillance system. We use a transition model that incorporates First-Order Logic relations and a two-phases Particle Filter algorithm in order to directly track relations between targets. We present some results about activity recognition in monitoring coastal borders.
Manfredotti, C., Messina, V., Fleet, D. (2009). Relations to improve multi-target tracking in an activity recognition system. In Proceedings of the 3rd International Conference on Imaging for Crime Detection and Prevention ICDP09 [10.1049/ic.2009.0254].
Relations to improve multi-target tracking in an activity recognition system
MANFREDOTTI, CRISTINA ELENA;MESSINA, VINCENZINA;
2009
Abstract
The explicit recognition of the relationships between interacting objects can improve the understanding of their dynamics. In this work, we investigate the use of Relational Dynamic Bayesian Networks to represent the interactions between moving objects in a surveillance system. We use a transition model that incorporates First-Order Logic relations and a two-phases Particle Filter algorithm in order to directly track relations between targets. We present some results about activity recognition in monitoring coastal borders.
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