  Situation AWareness (SAW) is essential for many mission critical
applications. However, SAW is very challenging when trying to immediately
identify objects of interest or zoom in on suspicious activities from thousands
of video frames. This work aims at developing a queryable system to instantly
select interesting content. While face recognition technology is mature, in
many scenarios like public safety monitoring, the features of objects of
interest may be much more complicated than face features. In addition, human
operators may not be always able to provide a descriptive, simple, and accurate
query. Actually, it is more often that there are only rough, general
descriptions of certain suspicious objects or accidents. This paper proposes an
Interactive Video Surveillance as an Edge service (I-ViSE) based on
unsupervised feature queries. Adopting unsupervised methods that do not reveal
any private information, the I-ViSE scheme utilizes general features of a human
body and color of clothes. An I-ViSE prototype is built following the edge-fog
computing paradigm and the experimental results verified the I-ViSE scheme
meets the design goal of scene recognition in less than two seconds.
