Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images
Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object rob...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/890619 |
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| author | A. F. M. Saifuddin Saif Anton Satria Prabuwono Zainal Rasyid Mahayuddin |
| author_facet | A. F. M. Saifuddin Saif Anton Satria Prabuwono Zainal Rasyid Mahayuddin |
| author_sort | A. F. M. Saifuddin Saif |
| collection | DOAJ |
| description | Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology. |
| format | Article |
| id | doaj-art-f7164b7c639542aba31cd3dbe74cc6ba |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f7164b7c639542aba31cd3dbe74cc6ba2025-02-03T01:03:11ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/890619890619Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial ImagesA. F. M. Saifuddin Saif0Anton Satria Prabuwono1Zainal Rasyid Mahayuddin2Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor Darul Ehsan, MalaysiaFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor Darul Ehsan, MalaysiaFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor Darul Ehsan, MalaysiaMotion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.http://dx.doi.org/10.1155/2014/890619 |
| spellingShingle | A. F. M. Saifuddin Saif Anton Satria Prabuwono Zainal Rasyid Mahayuddin Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images The Scientific World Journal |
| title | Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images |
| title_full | Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images |
| title_fullStr | Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images |
| title_full_unstemmed | Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images |
| title_short | Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images |
| title_sort | moving object detection using dynamic motion modelling from uav aerial images |
| url | http://dx.doi.org/10.1155/2014/890619 |
| work_keys_str_mv | AT afmsaifuddinsaif movingobjectdetectionusingdynamicmotionmodellingfromuavaerialimages AT antonsatriaprabuwono movingobjectdetectionusingdynamicmotionmodellingfromuavaerialimages AT zainalrasyidmahayuddin movingobjectdetectionusingdynamicmotionmodellingfromuavaerialimages |