A DEEP LEARNING BASED EFFICENT FIREARMS MONITORING TECHNIQUE FOR BUILDING SECURE SMART CITIES

Abstract

Crime prediction in video-surveillance systems is required to prevent incident and
protect assets. In this sense, our article proposes first artificial intelligence approach
for Robbery Behavior Potential (RBP) prediction and detection in an indoor camera.
Our method is based on three detection modules including head cover, crowd and
loitering detection modules for timely actions and preventing robbery. The two first
modules are implemented by retraining YOLOV5 model with our gathered dataset
which is annotated manually. In addition, we innovate a novel definition for loitering
detection module which is based on DeepSORT algorithm. A fuzzy inference
machine renders an expert knowledge as rules and then makes final decision about
predicted robbery potential. This is laborious due to: different manner of robber,
different angle of surveillance camera and low resolution of video images.We
accomplished our experiment on real world video surveillance images and reaching
the F1-score of 0.537. Hence, to make an experimental comparison with the other
related works, we define threshold value for RBP to evaluate video images as a
robbery detection problem. Under this assumption, the experimental results show that
the proposed method performs significantly better in detecting the robbery as
compared to the robbery detection methods by distinctly report with F1-score of 0.607.
We strongly believe that the application of the proposed method could cause
reduction of robbery detriment in a control center of surveillance cameras by
predicting and preventing incident of robbery. On the other hand, situational
awareness of human operator enhances and more cameras can be managed.