Memory consistency validation in a cognitive vision system

Ensuring the consistency of memory content is a key feature of cognitive vision systems. This work presents an approach to deal with functional dependencies of hypotheses stored in a visual active memory. By means of Bayesian networks a probabilistic approach is used to incorporate uncertainty of observations. Furthermore, a measurement to detect inconsistencies in the memory is introduced. The benefit of this validation module as part of an integrated system is shown for the task of visual surveillance in an office scenario.