PTZ Camera-Based Displacement Sensor System with Perspective Distortion Correction Unit for Early Detection of Building Destruction

Abstract

This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls the zoom level of the lens for a constant field of view (FOV). The proposed approach adopts perspective distortion correction to expand the measurable range in monitoring the displacement of the target structure. The implemented system successfully obtains the displacement information in structures, which is not easily accessible on the remote site. We manually measured the displacement acquired from markers which is attached on a sample of structures covering a wide geographic region. Our approach using a PTZ-based camera reduces the perspective distortion, so that the improved system could overcome limitations of previous works related to displacement measurement. Evaluation results show that a PTZ-based displacement sensor system with the proposed distortion correction unit is possibly a cost effective and easy-to-install solution for commercialization.

A Novel Laser and Video-Based Displacement Transducer to Monitor Bridge Deflections

“…Additionally, it is not necessary to re-scale the frame, because the physical distance between the LED lights is known (this is intrinsic information of the sensor) and the ratio between the distance between LED lights in pixels and in mm is obtained in each frame, as explained later. In consequence, the only correction to be applied is the homography transform [ 27 , 28 , 29 , 30 , 31 ]. This transform recovers the orthogonality between the video camera and the measurement panel.…”

Computer Vision-Based Bridge Displacement Measurements Using Rotation-Invariant Image Processing Technique

“…In object tracking, DIC is not an ideal technique as it can be easily affected by image scale and rotation [28]. In order to overcome the limitations of template-based matching using DIC technique, Jeong et al [29] proposed a pan-tilt-zoom (PTZ) camera-based computer vision system using a sum of squared differences (SSD) and homography-matrix [30], this system is capable of expanded measurable range in monitoring structural displacement. Till present, several correlation-coefficient functions have been introduced including normalized cross-correlation (NCC), sum of absolute differences (SAD), sum of squared differences (SSD), zero-mean normalized cross-correlation (ZNCC), zero-mean sum of absolute differences (ZSAD), zero-mean sum of squared differences (ZSSD), optimized sum of absolute difference (OSAD), and optimized sum of squared difference (OSSD) [31,32].…”

Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges

“…Previous studies have reported that computer vision-based methods possess the potential to address the issues in existing techniques [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. The existing vision-based methods differ by (1) non-target approaches, (2) feature detection, and (3) coordinate transforms.…”

“…Once a feature is detected, the position of the feature is transformed to the physical domain by using a coordinate transform. Several different transformation methods have been employed, such as simple scaling [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 40 , 44 ], the affine transform [ 36 , 37 ], extrinsic parameters acquisition [ 42 , 43 ], and the homography transform [ 46 , 47 , 48 , 49 , 50 ]. Previous studies have shown the immense potential of computer vision for displacement sensing and other SHM applications, such as system identification [ 51 ] and long-span bridge displacement measurement [ 52 ].…”

“…The non-target approaches are convenient in that they do not need an installation of target markers. Despite the convenience, target-based measurement becomes useful when combined with the homography transform, which can greatly increase field applicability by allowing cameras to be arbitrarily placed [ 46 , 47 , 48 , 49 , 50 ]. Regarding light-induced error, few studies have examined feature detection in a harsh field-testing environment, particularly those with adverse light conditions [ 53 ].…”