Hostile Intent Identification by Movement Pattern Analysis: Using Artificial Neural Networks

TL;DR

This paper proposes a neural network-based methodology for identifying hostile intent through movement pattern analysis, aiming to improve automated threat detection in tactical scenarios by learning from experience.

Contribution

It introduces a generalized, experience-learning approach using neural networks for hostile intent detection based on movement patterns, enhancing automation and accuracy.

Findings

Method successfully implemented in simulation

Potential applicability in real-world scenarios

Improves upon manual and existing automated solutions

Abstract

In the recent years, the problem of identifying suspicious behavior has gained importance and identifying this behavior using computational systems and autonomous algorithms is highly desirable in a tactical scenario. So far, the solutions have been primarily manual which elicit human observation of entities to discern the hostility of the situation. To cater to this problem statement, a number of fully automated and partially automated solutions exist. But, these solutions lack the capability of learning from experiences and work in conjunction with human supervision which is extremely prone to error. In this paper, a generalized methodology to predict the hostility of a given object based on its movement patterns is proposed which has the ability to learn and is based upon the mechanism of humans of learning from experiences. The methodology so proposed has been implemented in a computer simulation. The results show that the posited methodology has the potential to be applied in real world tactical scenarios.