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A spatio-temporal probabilistic model for multi-sensor object recognition

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3 Author(s)
Bertrand Douillard ; ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, University of Sydney, NSW, Australia ; Dieter Fox ; Fabio Ramos

This paper presents a general framework for multi-sensor object recognition through a discriminative probabilistic approach modelling spatial and temporal correlations. The algorithm is developed in the context of Conditional Random Fields (CRFs) trained with virtual evidence boosting. The resulting system is able to integrate arbitrary sensor information and incorporate features extracted from the data. The spatial relationships captured by are further integrated into a smoothing algorithm to improve recognition over time. We demonstrate the benefits of modelling spatial and temporal relationships for the problem of detecting cars using laser and vision data in outdoor environments.

Published in:

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

Date of Conference:

Oct. 29 2007-Nov. 2 2007