LAUSR

Abstract Vision-based displacement measurement receives increasing attention on non-contact bridge monitoring while it faces challenges in long-time field applications due to the presence of environmental variations. To overcome this issue, this study proposes a novel distraction-free displacement measurement approach by integrating deep learning-based Siamese tracker with correlation-based template matching. The Siamese tracker used applies deep feature representations and learned similarity measures for image matching and also considers adaptive template update with time. Since the estimated bounding boxes by the Siamese tracker have size changes within frame sequences, a correction step is added to remove the centroid drifts between the template and the predicted target regions using correlation-based template matching. The proposed method is validated first in an indoor test and then implemented in monitoring tests on a short-span footbridge and a long-span road bridge, demonstrating its potential to handle challenging scenarios including partial occlusion, illumination changes, background variations and shade effects.