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

TL;DR

This paper presents TACTIC-GRAPHS, a multimodal graph neural framework that improves semantic understanding and threat detection in tactical videos by integrating spectral graph theory, causal reasoning, and keyframe fusion across audio-visual data.

Contribution

The paper introduces a novel framework combining spectral graph theory, causal multimodal reasoning, and semantic-aware keyframe extraction for enhanced threat detection in noisy tactical video environments.

Findings

Achieved 89.3% accuracy in temporal alignment.

Recognized over 85% of complete threat chains.

Node latency within ±150 milliseconds.

Abstract

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.