Novel Deep Neural Network for Suspicious Activity Detection and Classification
A. M. Bhugul, Vijay S. Gulhane · 2023
Implementing preventive measures against gunshots and terrorist attacks in public areas with heavy foot traffic is crucial. Although security cameras are already commonplace, real-time reaction and 24/7 monitoring needed for automated detection techniques. As opposed to a weapon alone, a weapon in the hands of a human is thought to pose a larger threat. This paper has introduced a novel algorithm for multiple gun detection with an innovative deep learning neural network (DNN) model. The parameter for human suspicious activity in this paper is a person with a weapon(gun) and a person wearing a helmet with a weapon(gun). The temporal complexity of the Proposed System Architecture (PSA) on various hardware platforms is also explored. The algorithm gives 99% accuracy as compared with other existing methods.