FAMTrack: Frequency-Aware Matching for Vision-Based Seismic Intensity Prediction

Rapid seismic intensity estimation supports situational awareness, emergency response, and the assessment of earthquake-induced structural risk. Instrument-based systems provide authoritative measurements, but limited deployment density and installation cost can restrict spatial coverage. Video sensing offers a complementary route by converting the visible motion of a calibrated target into physical motion indicators; its reliability, however, depends on stable frame-wise localisation under illumination variation, blur, and target deformation. This paper presents FAMTrack, a vision-based seismic intensity prediction framework whose tracking module augments a one-stream Vision Transformer with a parallel Frequency-Aware Matching branch that encodes patch-wise Fourier amplitude and phase. A Cross-Domain Fusion Module exchanges spatial and frequency evidence through bidirectional cross-attention without modifying the detection head or tracking loss. On LaSOT, FAMTrack-256 attains a 75.1% AUC, 4.0 percentage points above OSTrack-256. On GOT-10K, it reaches 77.9% AO, a gain of 4.2 points. In the held-out seismic-video evaluation, FAMTrack reduces the displacement RMSE from 0.523 to 0.332 mm and improves window-level intensity prediction accuracy from 80.0% to 86.7% relative to OSTrack-256. Under the fixed calibrated-video pipeline and the evaluated controlled conditions, these results indicate that frequency-aware localisation is associated with improved public-benchmark tracking, motion recovery, and seismic intensity estimation.