AI and ML-Based Gesture Recognition and Communication System for Combat Vehicles in Adverse Environments
Deepak Dhillon, V Mariaprecilla, Rajaseeli Reginald, Aman Kataria · 2024
In contemporary warfare, communication breakdowns within armoured fighting vehicles present critical operational hurdles. Traditional audio channels are prone to jamming and interception, potentially jeopardizing the efficacy of military manoeuvres. This paper introduces an innovative gesture-based communication system specifically tailored for armoured fighting vehicles operating in hostile environments. The system seamlessly integrates flex sensors within gloves and harnesses a camera-based recognition framework to accurately interpret hand gestures. Employing Python and the MediaPipe library for gesture recognition and flex sensor data processing, this system establishes an alternative communication channel resilient against audio interception. Furthermore, through a dedicated machine learning algorithm, the system amalgamates inputs from both sensor types, ensuring rapid and reliable transmission of crucial messages. This solution endeavours to bolster communication reliability in armoured vehicles, furnishing a secure conduit for transmitting vital information to crew members or ground control stations while effectively mitigating risks linked to audio channel vulnerabilities.