Facility surveillance systems and methods

An automated surveillance method for a machine learned detection of a critical state of a facility, the method comprising the steps of:

• deriving multiple surveillance data from at least two surveillance sensors for the facility, which surveillance sensors are operating in at least two modalities in course of the automated surveillance of the facility,
• deriving at least one contextual information at the facility, in particular an environmental information by a context-sensing means,
• combining data from one or more of the surveillance sensors of which at least two are operating in different modalities for the detection of the critical state in course of the automated surveillance of the facility with a machine learned automated information filter comprising a context-adaptive model for data of different modalities, which is established by a training on training data which comprises the contextual information in a training phase in a plurality of extracted contexts,
• whereby in course of the training of the machine learned automated information filter contexts are learned and an optimal combining of surveillance-sensors of different modalities for the detection of a critical state is learned for these extracted contexts,
• deriving a parallel and/or hierarchical evaluation structure for the data from the surveillance sensors based on the machine learning in the plurality of contexts, whereby an optimal combination of the surveillance-sensors in hierarchical and/or parallel structures is learned automatically from data for each of the extracted contexts,
• wherein the combining is established by a hierarchical and/or parallel structure for merging the at least two surveillance sensors, wherein a weighting of the different sensing modalities is derived by the machine learning and dependent on the contextual information,
• and, after the training phase, using the learned optimal combining of surveillance-sensors of which at least two are operating in different modalities for the automated detection of states in course of said automated surveillance of the facility.