Abstract
Optical flow is a vision-based approach that is used for tracking the movement of objects. This robust technique can be an effective tool for determining the source of failures on slope surfaces, including the dynamic behavior of rockfall. However, optical flow-based measurement still remains an issue as the data from optical flow algorithms can be affected by the varied photographing environment, such as weather and illuminations. To address such problems, this paper presents an optical flow-based tracking algorithm that can be employed to extract motion data from video records for slope monitoring. Additionally, a workflow combined with photogrammetry and the optical flow technique has been proposed for producing highly accurate estimations of rockfall motion. The effectiveness of the proposed approach has been evaluated with the dataset obtained from a photogrammetry survey of field rockfall tests performed by the authors in 2015. The results show that the workflow adopted in this study can be suitable to identify rockfall events overtime in a slope monitoring system. The limitations of the current approach are also discussed.
Highlights
IntroductionHave been rapidly developed to investigate the instability of rock slopes
Motion of opticalofflow was using projected from based calculations introduced errors in the estimation of velocity. To overcome this limitation and to minimize the measurement errors, three-dimensional coordinates of the georeferenced 3D surface model generated from Sirovision were data regarding the velocity, which may be due to the low frame rate and shutter speed of the camera
It was observed that the current frame rate was too slow to capture the rapid rock object between successive frames
Summary
Have been rapidly developed to investigate the instability of rock slopes Among these techniques, vision-based technology, which includes inspection using captured records, image analysis, photogrammetry and real-time computer vision, has been regarded as the most effective and economical way to identify areas where inactive rock slopes have started to move [5,6,7,8,9,10]. Sets of images can provide a source of data to detect a failure on slope surface using deep learning algorithms such as convolutional neural networks (CNNs) [11]. These techniques have the drawbacks of using optics due to various external factors such as weather and dust, the vision-based approaches have the potential to continuously inspect the structural instability of rock slopes in real time.
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