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An assistive solution to assess incoming threats (e.g., robbery, burglary,gun violence) for homes will enhance the safety of the people with or withoutdisabilities. This paper presents "SafeNet"- an integrated assistive system togenerate context-oriented image descriptions to assess incoming threats. Thekey functionality of the system includes the detection and identification ofhuman and generating image descriptions from the real-time video streamsobtained from the cameras placed in strategic locations around the house. Inthis paper, we focus on developing a robust model called "SafeNet" to generateimage descriptions. To interact with the system, we implemented a dialogenabled interface for creating a personalized profile from face images orvideos of friends/families. To improve computational efficiency, we applychange detection to filter out frames that do not have any activity and useFaster-RCNN to detect the human presence and extract faces using MultitaskCascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP/FaceNet toidentify a person. SafeNet sends image descriptions to the users with an MMScontaining a person's name if any match found or as "Unknown", scene image,facial description, and contextual information. SafeNet identifiesfriends/families/caregiver versus intruders/unknown with an average F-score0.97 and generates image descriptions from 10 classes with an average F-measure0.97.

SafeNet: An Assistive Solution to Assess Incoming Threats for Premises