A novel DNN tracking algorithm for structural system identification

Abstract

In the field of structural health monitoring (SHM), cameras record videos and tracking methods can be applied to calculate the structural displacement. Commercial and unmanned aerial vehicle (UAV) cameras are promising non-contact sensors owning to their high availability and easy installation. However, effective tracking methods need to be developed. In this study, we firstly propose an end-to-end vision measuring framework with a novel deep neural network (DNN) tracker, named Siamese Single Decoder Network (SiamSDN). The system requires no target installation and uses cellphone cameras. For SiamSDN, the position and scale of bounding box are formulated through statistical parameter estimation. Unlike generative trackers, SiamSDN does not require manually extracted features or pre-defined motion areas. The tracking object is solely identified in the first frame. A shaking table test of a five-storey structure is carried out to demonstrate the efficiency. Besides, a UAV is used to simulate the field test. To minimize the error caused by the vibrations of UAV, digital video stabilization (DVS) is proposed to eliminate the drifts. Videos taken by both the commercial and UAV cameras are analyzed to calculate the displacements. Comparing our DNN tracker with feature point matching approach, SiamSDN improves the displacement measuring accuracy by 66.16% and 57.54%, respectively, and the frequency characteristics are obtained precisely.