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Long-term Structural Displacement Monitoring using Image Sequences and Spatio-Temporal Context Learning

CHUAN-ZHI DONG, OZAN CELIK, F. NECATI CATBAS

Abstract


In this study, a vision-based displacement measurement method using image sequences and spatio-temporal context (STC) learning is introduced for long-term structural displacement monitoring. Comparative study is carried out to verify the feasibility of the proposed method with current vision-based displacement measurement methods including (DIC, FLANN-SURF and LK-SURF) and the ground truth from LVDT under different adverse measuring conditions (including illumination changes and random occlusion induced by artificial mist). The results show that the proposed method has better robustness to illumination changes and random occlusion than current vision-based methods. The proposed method is promising in handling long-term structural displacement monitoring task in field.


DOI
10.12783/shm2017/14233

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