I-ViSE: Interactive Video Surveillance as an Edge Service Using Unsupervised Feature Queries

Abstract

Situation awareness (SAW) is essential for many mission-critical applications. However, SAW is challenging when trying to immediately identify objects of interest or focus on suspicious activities from thousands of video frames. This article develops a queryable system to instantly select interesting content. While face recognition technology is mature, in many scenarios, such as 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 article proposes 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 and target analysis in less than 2 s.