  Urban imagery usually serves as forensic analysis and by design is available
for incident mitigation. As more imagery collected, it is harder to narrow down
to certain frames among thousands of video clips to a specific incident. A
real-time, proactive surveillance system is desirable, which could instantly
detect dubious personnel, identify suspicious activities, or raise momentous
alerts. The recent proliferation of the edge computing paradigm allows more
data-intensive tasks to be accomplished by smart edge devices with lightweight
but powerful algorithms. This paper presents a forensic surveillance strategy
by introducing an Instant Suspicious Activity identiFication at the Edge
(I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to
mimic the decision-making process of a security officer. Decisions are made
based on video features extracted by a lightweight Deep Machine Learning (DML)
model. Based on the requirements from the first-line law enforcement officers,
several features are selected and fuzzified to cope with the state of
uncertainty that exists in the officers' decision-making process. Using
features in the edge hierarchy minimizes the communication delay such that
instant alerting is achieved. Additionally, leveraging the Microservices
architecture, the I-SAFE scheme possesses good scalability given the increasing
complexities at the network edge. Implemented as an edge-based application and
tested using exemplary and various labeled dataset surveillance videos, the
I-SAFE scheme raises alerts by identifying the suspicious activity in an
average of 0.002 seconds. Compared to four other state-of-the-art methods over
two other data sets, the experimental study verified the superiority of the
I-SAFE decentralized method.
