NDLI: Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

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Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

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Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Content Provider	Tech Science Press
Author	Sankaranarayanan, G. Kumar, M. Pradeep Raja, V. K. Bupesh Ganesan, R. Kumar, M.
Abstract	This research introduces a challenge in integrating and cleaning the data, which is a crucial task in object matching. While the object is detected and then measured, the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage, abnormal stopping, and disaster. Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement. To solve all these problems, this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image. A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation (UCC) algorithm. The vibration measurement is calculated using the Finite-Difference Algorithm (FDA), a machine vision system fitted with a macro lens sensor that is capable of capturing the image at a closer range, which does not affect the quality of displacement measurement from the video frames. The field test was conducted on the TAFE (Tractors and Farm Equipment Limited) tractor parts, and the percentage of error was recorded between 30% and 50% at very low vibration values close to zero, whereas it was recorded between 5% and 10% error in most high-accelerations, the essential range for vibration analysis. Finally, the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges. The use of a macro lens enables to capture of image frames at very close-ups. A 30% to 50% error percentage has been reported when the vibration amplitude is very small. Therefore, this study is not suitable for Nano vibration analysis.
Related Links	https://www.techscience.com/iasc/v36n1/50004
Starting Page	667
Ending Page	681
ISSN	10798587
DOI	10.32604/iasc.2023.031932
Issue Number	1
Journal	Intelligent Automation & Soft Computing (IASC)
Volume Number	36
e-ISSN	2326005X
Language	English
Publisher Date	2022-09-29
Access Restriction	Open
Subject Keyword	macro lens template matching object tracking up-sampled cross-correlation Vibration measurement finite difference algorithm machine vision
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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