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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Main Authors: Liana Ellen Taylor, Midriem Mirdanies, Roni Permana Saputra
Format: Article
Language:English
Published: Indonesian Institute of Sciences 2016-07-01
Series:Journal of Mechatronics, Electrical Power, and Vehicular Technology
Subjects:
Online Access:http://mevjournal.com/index.php/mev/article/view/287
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spelling 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
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