A novel gradient-based matching via voting technique for vision-based structural displacement measurement. (15th May 2022)

• Record Type: Journal Article
• Authors: Wang, Miaomin; Ao, Wai Kei; Bownjohn, James; Xu, Fuyou
• Is Part Of: Mechanical systems and signal processing. Volume 171(2022)
• Publication Date: 2022-05-15
• Subjects: Structural displacement measurement -- Vision-sensing system -- Gradient-based Matching via Voting -- Incomplete target; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.108951
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A tracking technique is proposed to track incomplete moved targets. The technique can robustly track a moved target with 90% feature losses. Superiority of the technique over traditional template matching techniques are studied. Accuracy and robustness of the technique are validated through three tests. Abstract: The effectiveness of vision-sensing system for field applications in remote measurements of structural displacement can be adversely affected by environmental factors, leading to an incomplete recording of a moving feature target in recorded video frames. The result is a non-ideal solution using traditional template matching techniques, which presents significant challenges for accurate measurements. This paper proposes a novel gradient-based matching via voting (GMV) technique to overcome environmental and operational conditions and provide reliable tracking of moved targets with different degrees of feature loss. When evaluating the region similarity between the template and a subset (called the overlapping region) of video frames in which the moved target is located to obtain a similarity score of the subset, the traditional template matching does not ascribe weights to the similarity at the pixel level, whereas GMV uses a voting scheme to weight all the pixel similarities. Three experiments were used to test GMV, evaluating its tracking accuracy and verifying its robustness with different levels of target feature loss based on a comparison with two commonly used template matching techniques. GMV proved to be capable of retrieving accurate displacement data with a high degree of accuracy, even for a moved target with up to 90% feature losses.