KINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING
In the purposes of education and physics experiments, sensors are now widely used that have not been able to provide kinematics interpretation of the motion vectors of objects, but only scalar quantities. Image processing can be a solution to these problems. Color feature-based object detection a...
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id-itb.:744022023-07-12T14:30:52ZKINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING Haamesha, Avima Indonesia Final Project image processing, kinematics, tracking, validation INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/74402 In the purposes of education and physics experiments, sensors are now widely used that have not been able to provide kinematics interpretation of the motion vectors of objects, but only scalar quantities. Image processing can be a solution to these problems. Color feature-based object detection and tracking algorithms are approaches used in the design and implementation of systems that are built so that they obtain output in the form of software applications for 2D kinematics analysis. The kinematics of objects can be obtained with at least 3 frames (???2, ???1, ??) by utilizing the finite difference method. In the validation of program results against sensor data, the selection of object geometry parameters is important because it affects the results of kinematics data. COV conversion amount is obtained from the least geometric parameters experiencing motion blur. The kinematics results in the case of the Atwood plane satisfy the straight-motion model with fixed acceleration for the directional component data ??, while the pendulum oscillation case satisfies the damped oscillation motion for the directional component data ??. In the comparison of gravity relative error, the program results showed better accuracy than sensors, namely 27,472% for category A and 4,017% for category D. Meanwhile, in the case of pendulum aircraft, a relative gravity error of 10,1113% was obtained. text |
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In the purposes of education and physics experiments, sensors are now widely used
that have not been able to provide kinematics interpretation of the motion vectors
of objects, but only scalar quantities. Image processing can be a solution to these
problems. Color feature-based object detection and tracking algorithms are
approaches used in the design and implementation of systems that are built so that
they obtain output in the form of software applications for 2D kinematics analysis.
The kinematics of objects can be obtained with at least 3 frames (???2, ???1, ??) by
utilizing the finite difference method. In the validation of program results against
sensor data, the selection of object geometry parameters is important because it
affects the results of kinematics data. COV conversion amount is obtained from the
least geometric parameters experiencing motion blur. The kinematics results in the
case of the Atwood plane satisfy the straight-motion model with fixed acceleration
for the directional component data ??, while the pendulum oscillation case satisfies
the damped oscillation motion for the directional component data ??. In the
comparison of gravity relative error, the program results showed better accuracy
than sensors, namely 27,472% for category A and 4,017% for category D.
Meanwhile, in the case of pendulum aircraft, a relative gravity error of 10,1113%
was obtained. |
format |
Final Project |
author |
Haamesha, Avima |
spellingShingle |
Haamesha, Avima KINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING |
author_facet |
Haamesha, Avima |
author_sort |
Haamesha, Avima |
title |
KINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING |
title_short |
KINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING |
title_full |
KINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING |
title_fullStr |
KINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING |
title_full_unstemmed |
KINEMATICS ANALYSIS OF OBJECT USING DIGITAL IMAGE PROCESSING |
title_sort |
kinematics analysis of object using digital image processing |
url |
https://digilib.itb.ac.id/gdl/view/74402 |
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