Relations to improve multi-target tracking in an activity recognition system
Relations to improve multi-target tracking in an activity recognition system
- Author(s): C.E. Manfredotti ; D. Fleet ; E. Messina
- DOI: 10.1049/ic.2009.0254
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- Author(s): C.E. Manfredotti ; D. Fleet ; E. Messina Source: 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), 2009 page ()
- Conference: 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)
- DOI: 10.1049/ic.2009.0254
- ISBN: 978 1 84919 207 1
- Location: London, UK
- Conference date: 3 Dec. 2009
- Format: PDF
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. (6 pages)
Inspec keywords: surveillance; particle filtering (numerical methods); target tracking; belief networks; object recognition
Subjects: Knowledge engineering techniques; Combinatorial mathematics; Combinatorial mathematics; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis); Image recognition
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