The prediction of seismic intensity in an accurate and timely manner is needed to provide scientific guidance for disaster relief. Traditional seismic intensity prediction methods rely on seismograph equipment, which is limited by slow response times and high equipment costs. In this study, we introduce a low-computational-cost transformer-based (LCCTV) visual tracking method to predict seismic intensity in surveillance videos. To this end, an earthquake video dataset is proposed. It is captured in the laboratory environment, where the seismic level is obtained through seismic station simulation. With the proposed dataset, a low-computational-cost transformer-based visual tracking method is first proposed to estimate the movement trajectory of the calibration board target in videos in real time. In order to further improve the recognition accuracy, we then utilize a Butterworth filter to smooth the generated movement trajectory so as to remove low-frequency interference signals. Finally, the seismic intensity is predicted based on the velocity and acceleration derived from the smoothed movement trajectory. Experimental results demonstrated that the LCCTV outperformed other state-of-the-art approaches. The findings confirm that the proposed LCCTV can serve as a low-cost, scalable solution for seismic intensity analysis.