http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#Head http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY http://www.nanopub.org/nschema#hasAssertion http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#assertion http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY http://www.nanopub.org/nschema#hasProvenance http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#provenance http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY http://www.nanopub.org/nschema#hasPublicationInfo http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#pubinfo http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#assertion http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#paragraph http://purl.org/spar/c4o/hasContent Figure 2 illustrates the architecture of the WiseNET system. The system is articulated in three sections: the smart camera network, the central unit and the monitor unit. The SCN is a set of smart cameras distributed in an environment. The main functions of the SCN, in the WiseNET system, is to extract low-level features from a scene (such as person detection as shown in blue and green in the right side of the Figure 2), to convert the extracted data into knowledge and then to send it to the central unit. More information regarding the type of smart cameras used can be found in [23]. The central unit is composed of two elements the central API and the WiseNET ontology. The central API is in charge of the management of the ontology, for example: capturing the knowledge coming from the SCN and insert it to the ontology, retrieving inferred knowledge from the ontology, transferring data to the monitor unit, sending new configurations to the smart cameras, and other services. The WiseNET ontology is responsible of enabling interoperability between the incoming knowledge streams and the contextual data (e.g., environment information, previous knowledge and sensor in- formation) in order to deduce new knowledge and detect events/anomalies based on the history of activities. The central unit is also in charge of re-identifying people in the system by using their visual features extracted by the SCN. Eventually, the central unit could request extra information to the SCN. The monitor unit has as main function the visualization of the static and dynamic information; this unit will automatically retrieve information and will present it in a graphical manner, for example an occupancy map (as shown at the center of Figure 2). The monitor unit implements some queries to answer questions such as: how many people is in a room? what is the location of a person? and many others (see Table 1). http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#paragraph http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/doco/Paragraph http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#provenance http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#assertion http://www.w3.org/ns/prov#hadPrimarySource http://dx.doi.org/10.3233/SW-180298 http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0000-0002-8429-8208 http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY#pubinfo http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY http://purl.org/dc/terms/created 2019-11-08T18:05:11+01:00 http://purl.org/np/RAwqRsp6-rqsNpYRxE0WeXwFXAww9WmhNMx6A3N3gGlHY http://purl.org/pav/createdBy https://orcid.org/0000-0002-7114-6459