Speaker
Description
Modern military operations routinely unfold under low visibility and strict concealment constraints. In these conditions, estimating time-since-discharge and recent firing patterns from passive sensing alone - without active illumination or other emitters - is critical to reduce misidentification and associated casualties. Passive IR observations, particularly thermal imagery, can preserve stealth while providing actionable cues about a weapon’s recent activity and thermal state, supporting rapid decisions that mitigate civilian harm and the risk of friendly-fire incidents.
Contemporary modeling offers a broad spectrum of approaches for object recognition and feature extraction, each balancing robustness, domain transfer, and interpretability. In this work, we prioritize reliability and physical interpretability by analyzing localized thermal measurements through heat-dissipation models to estimate recent discharge activity and support rapid assessment in low-visibility scenarios (assuming prior identification of the firearm). Because the core inference relies on model-based and classical vision routines rather than learned representations, the method transfers across sensing platforms and firearm geometries with minimal re-parameterization, avoiding extensive retraining when deployed to new configurations.
Our approach analyzes passive thermal imagery using heat-dissipation models to estimate recent discharge activity and support time-critical assessment. The dataset comprises thermal sequences from multiple firearm platforms with key structural features, such as the disposition of moving parts (influencing overall heat transfer) and the material thickness (mainly associated with heat capacity), so that results can be easily extrapolated to similar devices, ranging from low-energy ammunitions (9mm) to high-energy ammunitions (.357 Magnum).
Preliminary results show the viability of obtaining characteristic thermal decay curves for each analyzed firearm model, thus establishing weapon-specific decay profiles, depending on discharge parameters. These profiles allow for temporal estimation for tactical decision-making processes, where discrimination between weapons fired after various time frames is achievable. The resulting database of thermal-temporal signatures for military firearms provides a significant milestone towards developing autonomous classification systems.
The results foreground the potential of employing physics-informed analytics as a means of facilitating an assessment of discharge events in low-visibility conditions, passively and covertly. The methodology has direct utility in verification of rules of engagement, forensic analysis and real-time threat assessment systems to minimize incidents in complex battlespaces, where the identification of combatants and non-combatants remains an enduring challenge.
