Long-Term Structural Displacement Monitoring Using Image Sequences And Spatio-Temporal Context Learning

Abstract

In this study, a vision-based displacement measurement method using imatge 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.

Publication Date

1-1-2017

Publication Title

Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017

Volume

2

Number of Pages

3217-3223

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.12783/shm2017/14233

Socpus ID

85032334483 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85032334483

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