  An assistive solution to assess incoming threats (e.g., robbery, burglary,
gun violence) for homes will enhance the safety of the people with or without
disabilities. This paper presents "SafeNet"- an integrated assistive system to
generate context-oriented image descriptions to assess incoming threats. The
key functionality of the system includes the detection and identification of
human and generating image descriptions from the real-time video streams
obtained from the cameras placed in strategic locations around the house. In
this paper, we focus on developing a robust model called "SafeNet" to generate
image descriptions. To interact with the system, we implemented a dialog
enabled interface for creating a personalized profile from face images or
videos of friends/families. To improve computational efficiency, we apply
change detection to filter out frames that do not have any activity and use
Faster-RCNN to detect the human presence and extract faces using Multitask
Cascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP/FaceNet to
identify a person. SafeNet sends image descriptions to the users with an MMS
containing a person's name if any match found or as "Unknown", scene image,
facial description, and contextual information. SafeNet identifies
friends/families/caregiver versus intruders/unknown with an average F-score
0.97 and generates image descriptions from 10 classes with an average F-measure
0.97.
