Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera

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.