Optimized object tracking technique using Kalman filter
This paper focused on the design of an optimized object tracking technique which would minimize the processing time required in the object detection process while maintaining accuracy in detecting the desired moving object in a cluttered scene. A Kalman filter based cropped image is used for the ima...
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Indonesian Institute of Sciences
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Online Access: | http://mevjournal.com/index.php/mev/article/view/287 |
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doaj-5791f8ed8cc3491f92a2ab4e316fa1312020-11-25T01:21:33ZengIndonesian Institute of SciencesJournal of Mechatronics, Electrical Power, and Vehicular Technology2087-33792088-69852016-07-01715766http://dx.doi.org/10.14203/j.mev.2016.v7.57-66Optimized object tracking technique using Kalman filterLiana Ellen Taylor0Midriem Mirdanies1Roni Permana Saputra2School of Engineering and Information Technology - University of New South Wales (UNSW) Canberra, AustraliaResearch Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences (LIPI), IndonesiaResearch Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences (LIPI), IndonesiaThis paper focused on the design of an optimized object tracking technique which would minimize the processing time required in the object detection process while maintaining accuracy in detecting the desired moving object in a cluttered scene. A Kalman filter based cropped image is used for the image detection process as the processing time is significantly less to detect the object when a search window is used that is smaller than the entire video frame. This technique was tested with various sizes of the window in the cropping process. MATLAB® was used to design and test the proposed method. This paper found that using a cropped image with 2.16 multiplied by the largest dimension of the object resulted in significantly faster processing time while still providing a high success rate of detection and a detected center of the object that was reasonably close to the actual center.http://mevjournal.com/index.php/mev/article/view/287Kalman filterobject trackingobject detectioncroppingcolor segmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liana Ellen Taylor Midriem Mirdanies Roni Permana Saputra |
spellingShingle |
Liana Ellen Taylor Midriem Mirdanies Roni Permana Saputra Optimized object tracking technique using Kalman filter Journal of Mechatronics, Electrical Power, and Vehicular Technology Kalman filter object tracking object detection cropping color segmentation |
author_facet |
Liana Ellen Taylor Midriem Mirdanies Roni Permana Saputra |
author_sort |
Liana Ellen Taylor |
title |
Optimized object tracking technique using Kalman filter |
title_short |
Optimized object tracking technique using Kalman filter |
title_full |
Optimized object tracking technique using Kalman filter |
title_fullStr |
Optimized object tracking technique using Kalman filter |
title_full_unstemmed |
Optimized object tracking technique using Kalman filter |
title_sort |
optimized object tracking technique using kalman filter |
publisher |
Indonesian Institute of Sciences |
series |
Journal of Mechatronics, Electrical Power, and Vehicular Technology |
issn |
2087-3379 2088-6985 |
publishDate |
2016-07-01 |
description |
This paper focused on the design of an optimized object tracking technique which would minimize the processing time required in the object detection process while maintaining accuracy in detecting the desired moving object in a cluttered scene. A Kalman filter based cropped image is used for the image detection process as the processing time is significantly less to detect the object when a search window is used that is smaller than the entire video frame. This technique was tested with various sizes of the window in the cropping process. MATLAB® was used to design and test the proposed method. This paper found that using a cropped image with 2.16 multiplied by the largest dimension of the object resulted in significantly faster processing time while still providing a high success rate of detection and a detected center of the object that was reasonably close to the actual center. |
topic |
Kalman filter object tracking object detection cropping color segmentation |
url |
http://mevjournal.com/index.php/mev/article/view/287 |
work_keys_str_mv |
AT lianaellentaylor optimizedobjecttrackingtechniqueusingkalmanfilter AT midriemmirdanies optimizedobjecttrackingtechniqueusingkalmanfilter AT ronipermanasaputra optimizedobjecttrackingtechniqueusingkalmanfilter |
_version_ |
1725129533841997824 |