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The paper introduces FLO-EMD, a hybrid framework for traffic congestion classification that combines motion-guided attention with empirical mode decomposition to capture both spatial context and non-stationary traffic motion. Dense optical flow guides attention to motion-relevant regions in RGB features, while empirical mode decomposition extracts intrinsic temporal components from aggregated flow statistics. Experiments on surveillance network data demonstrate that FLO-EMD achieves 97.5% accuracy, outperforming baselines and demonstrating robustness across diverse conditions.
Achieve near-perfect traffic congestion classification by fusing motion-guided visual attention with data-adaptive temporal decomposition, outperforming existing vision-based and signal-based methods.
Accurate traffic congestion classification requires models that jointly capture roadway scene context and non-stationary traffic motion, yet most prior work treats these requirements in isolation. Vision-based methods often depend on appearance cues with standard temporal pooling, which can bias predictions toward static infrastructure, whereas signal-based approaches characterize temporal dynamics but lack the spatial context needed for scene-level localization. These complementary limitations motivate a unified framework that links motion evidence to spatial feature selection while preserving data-adaptive temporal characterization. This study therefore proposes FLO-EMD, a hybrid approach that couples motion-guided attention with empirical, data-driven temporal decomposition. Dense optical flow guides channel and spatial attention so that RGB features are refined toward motion-relevant regions. In parallel, aggregated flow statistics form compact motion traces that are decomposed using Empirical Mode Decomposition (EMD) to extract intrinsic temporal components. The resulting EMD embedding is fused with learned spatiotemporal representations to classify light, medium, and heavy congestion. Experiments on 1,050 five-second clips from four surveillance networks show that FLO-EMD achieves 97.5% overall test accuracy (weighted F1 = 0.9742), outperforming established baselines and remaining robust across diverse environmental conditions; ablation and sensitivity analyses further quantify the contributions of EMD, the number of intrinsic mode functions, and the selected motion descriptors.
