TopoRelTrack: A Self-Supervised Visual Tracking Method for Video-Based Seismic Motion Measurement

Accurate target tracking is essential for video-based seismic-motion measurement, as tracking errors can directly affect displacement reconstruction and subsequent seismic-intensity estimation. To improve tracking accuracy and robustness under vibration scenarios, this paper proposes TopoRelTrack, a visual tracking method based on self-supervised pre-training. In the pre-training stage, a template-guided masked autoencoder (TMAE) uses the target template to guide masked reconstruction in the search region, enabling the model to learn tracking-oriented feature representations from unlabeled video sequences. A topology-preserving scanning Mamba (TPSMamba) is further introduced to efficiently capture long-range spatial dependencies while preserving spatial structural information. In addition, a sparse dynamic relation matcher (SDRM) establishes adaptive and fine-grained correspondences between the template and search regions to enhance target discrimination and suppress background interference. The TPSMamba and SDRM are then transferred to the downstream tracking network for continuous target localization and displacement reconstruction. Experiments on the LaSOT and GOT-10K benchmarks show that TopoRelTrack achieves an area under the curve (AUC) of 75.9% and a precision of 81.8% on LaSOT, as well as an AO of 78.5% on GOT-10K, outperforming the compared trackers. In controlled shaking-table video experiments, the proposed method also achieves lower displacement reconstruction errors and improved seismic-intensity prediction performance. The results demonstrate that TopoRelTrack provides accurate and robust target tracking for video-based motion measurement under controlled shaking-table excitation.