Hostile Intent Identification by Movement Pattern Analysis: Using Artificial Neural Networks
This addresses the need for automated, learning-based systems to identify suspicious behavior in tactical scenarios, reducing reliance on error-prone human supervision, though it appears incremental as it builds on existing automated solutions.
The paper tackles the problem of identifying hostile intent by analyzing movement patterns, proposing a generalized methodology that learns from experiences and is implemented in a computer simulation, showing potential for real-world tactical applications.
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.