A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms
This paper presents a novel object tracking framework for interest point based feature extracting algorithms. The proposed framework uses the feature extracting algorithm without making any changes and it relies on outlier detection, object modelling, and object tracking. At first, the keypoints are...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2020-02-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | http://eejournal.ktu.lt/index.php/elt/article/view/25311 |
| _version_ | 1818773087396560896 |
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| author | Zafer Guler Ahmet Cinar Erdal Ozbay |
| author_facet | Zafer Guler Ahmet Cinar Erdal Ozbay |
| author_sort | Zafer Guler |
| collection | DOAJ |
| description | This paper presents a novel object tracking framework for interest point based feature extracting algorithms. The proposed framework uses the feature extracting algorithm without making any changes and it relies on outlier detection, object modelling, and object tracking. At first, the keypoints are extracted by using a feature extraction algorithm. Then, incorrect keypoint matches are detected by the DBScan algorithm. The second step of our tracking framework is object modelling. The object model is defined as a bounding box. The box model has six points and each of these points has its own Gaussian model. Finally, the Gaussian model is performed for object tracking. In object tracking, the old five values are retained to detect incorrect position information. Thus, while the object movements are softened, the instant deviations are eliminated also. Our interest point based object tracking framework (IPBOT) works with any interest point based feature extracting algorithm. Thus, a new algorithm can be added to the object tracking framework with a short integration process. The experiment results show that the proposed tracker significantly improves the success rate of the object tracking. |
| first_indexed | 2024-12-18T10:19:40Z |
| format | Article |
| id | doaj.art-30a3eea9e0f6485b887e20f32b07b8cc |
| institution | Directory Open Access Journal |
| issn | 1392-1215 2029-5731 |
| language | English |
| last_indexed | 2024-12-18T10:19:40Z |
| publishDate | 2020-02-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj.art-30a3eea9e0f6485b887e20f32b07b8cc2022-12-21T21:11:10ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312020-02-01261637110.5755/j01.eie.26.1.2531125311A New Object Tracking Framework for Interest Point Based Feature Extraction AlgorithmsZafer GulerAhmet CinarErdal OzbayThis paper presents a novel object tracking framework for interest point based feature extracting algorithms. The proposed framework uses the feature extracting algorithm without making any changes and it relies on outlier detection, object modelling, and object tracking. At first, the keypoints are extracted by using a feature extraction algorithm. Then, incorrect keypoint matches are detected by the DBScan algorithm. The second step of our tracking framework is object modelling. The object model is defined as a bounding box. The box model has six points and each of these points has its own Gaussian model. Finally, the Gaussian model is performed for object tracking. In object tracking, the old five values are retained to detect incorrect position information. Thus, while the object movements are softened, the instant deviations are eliminated also. Our interest point based object tracking framework (IPBOT) works with any interest point based feature extracting algorithm. Thus, a new algorithm can be added to the object tracking framework with a short integration process. The experiment results show that the proposed tracker significantly improves the success rate of the object tracking.http://eejournal.ktu.lt/index.php/elt/article/view/25311feature extractionobject trackingsiftsurf |
| spellingShingle | Zafer Guler Ahmet Cinar Erdal Ozbay A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms Elektronika ir Elektrotechnika feature extraction object tracking sift surf |
| title | A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms |
| title_full | A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms |
| title_fullStr | A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms |
| title_full_unstemmed | A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms |
| title_short | A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms |
| title_sort | new object tracking framework for interest point based feature extraction algorithms |
| topic | feature extraction object tracking sift surf |
| url | http://eejournal.ktu.lt/index.php/elt/article/view/25311 |
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