A Spatio-Temporal
Probabilistic Model for Multi-Sensor Object Recognition
Proc. of IROS, 2007
Abstract
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.