Mast Arm Monitoring via Traffic Camera Footage: A Pixel-Based Modal Analysis Approach

Abstract

Traffic signal mast arm structures must be regularly inspected for cracking, bolt loosening, and other signs of deterioration. Due to large inventories, physical inspections and/or dedicated monitoring systems can be prohibitively time-consuming and expensive to implement at a large scale. However, the growing use of vision-based methods for structural monitoring applications introduces the possibility of leveraging video footage from existing traffic cameras for this purpose. The extraction of dynamic properties (i.e., natural frequencies and damping) from this footage could be employed in detecting possible signs of deterioration. This study presents a vision-based monitoring method which uses a single traffic camera to identify the modal properties of the supporting traffic signal mast arm. This was achieved via operational modal analysis on pixel displacements obtained from a traffic camera mounted on a traffic signal mast arm in Norfolk, VA, monitored during July, 2019. First, sub-pixel displacements were extracted frame-by-frame using weighted centroid tracking of pavement markings. Then, covariance-driven stochastic subspace identification (SSI-Cov) was employed to extract the mast arm fundamental frequencies, damping ratios, and mode shapes. For validation of the vision-based results, SSI-Cov was also applied to acceleration data recorded by two high-sensitivity accelerometers mounted on the structure. In total, the processing was carried out on four different videos and ten acceleration datasets. The vision-based method was able to reliably identify the fundamental frequencies of the structure (Δf < 0.005 Hz mean difference). The associated damping ratios were consistently overestimated but still close in structural terms (Δζ < 0.7% mean difference).