Evaluation of Auto Pilot Situational Awareness System Using Gunshot Detection Algorithm in a Localized Environment: Case Study Federal Polytechnic Offa, Mini Campus

Gunshot detection technologies are more applicable in many industries for the security enhancement of public places like the Federal Republic Territory FCT Abuja in Nigeria. Many factors affect the accuracy of the gun detection algorithm. This paper describes an audio-based video surveillance system in an auto pilot situational awareness to detect gunshots in Federal Polytechnic Offa. The Time Difference of Arrival (TDOA) of Shock Wave and Muzzle Blast is integrated to estimate the shooter location in the study are. The proposed design and algorithm was validated and shooter origin was resolute that was very close to theoretical values. The video camera is steering regarding the initial position to localize the acoustic source's position. Implementing an auto pilot situational awareness system is an experimental procedure with a gunshot detection algorithm in a localized environment. In the direction of the weapon, the distance between firearms, types of ammunition, types of study environment, and diffraction of audio, the standard feature for gunshot recognition are Mel frequency cepstral coefficients in terms of uniform gamma-tone filters linearly spaced over the whole frequency range from 0KHZ to 16KHZ. Experiments show that our system can detect gunshots with a precision of 93% at a false rejection rate of 5% when the SNR is 10db while proving the estimate of the source direction of the gunshot with an accuracy of one degree. The outcomes reveal that the data generated by the system can be leveraged by the firefighting department to quickly locate the whereabouts of the indoor fires, and the VR gamification scenarios can expedite the development of situational awareness for the trainees. The research recommends a real-time system implementation for protecting the Federal Polytechnic Offa against any form of treats.