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.

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.
paper
tracking; particle filtering;
English
Imaging for crime detection and prevention
2009
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.
Manfredotti, C; Messina, V; Fleet, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/8884
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