A Tactical Behaviour Recognition Framework Based on Causal Multimodal Reasoning: A Study on Covert Audio-Video Analysis Combining GAN Structure Enhancement and Phonetic Accent Modelling
This addresses threat detection for surveillance, defense, and security systems, but it appears incremental as it builds on existing multimodal and graph-based methods.
The paper tackles the problem of threat detection in tactical video under high noise and weak structure by introducing TACTIC-GRAPHS, a system that combines spectral graph theory and multimodal graph neural reasoning, achieving 89.3% accuracy in temporal alignment and over 85% recognition of complete threat chains.
This paper introduces TACTIC-GRAPHS, a system that combines spectral graph theory and multimodal graph neural reasoning for semantic understanding and threat detection in tactical video under high noise and weak structure. The framework incorporates spectral embedding, temporal causal edge modeling, and discriminative path inference across heterogeneous modalities. A semantic-aware keyframe extraction method fuses visual, acoustic, and action cues to construct temporal graphs. Using graph attention and Laplacian spectral mapping, the model performs cross-modal weighting and causal signal analysis. Experiments on TACTIC-AVS and TACTIC-Voice datasets show 89.3 percent accuracy in temporal alignment and over 85 percent recognition of complete threat chains, with node latency within plus-minus 150 milliseconds. The approach enhances structural interpretability and supports applications in surveillance, defense, and intelligent security systems.