Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (Pn...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-4893/11/8/122 |
| _version_ | 1818516559569616896 |
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| author | Chi-Yi Tsai Kuang-Jui Hsu Humaira Nisar |
| author_facet | Chi-Yi Tsai Kuang-Jui Hsu Humaira Nisar |
| author_sort | Chi-Yi Tsai |
| collection | DOAJ |
| description | Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality. |
| first_indexed | 2024-12-11T00:43:56Z |
| format | Article |
| id | doaj.art-4c907052ce694b1982e91166ee63d2b0 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-11T00:43:56Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-4c907052ce694b1982e91166ee63d2b02022-12-22T01:26:50ZengMDPI AGAlgorithms1999-48932018-08-0111812210.3390/a11080122a11080122Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP AlgorithmsChi-Yi Tsai0Kuang-Jui Hsu1Humaira Nisar2Department of Electrical and Computer Engineering, Tamkang University, 151 Ying-chuan Road, Danshui District, New Taipei City 251, TaiwanDepartment of Electrical and Computer Engineering, Tamkang University, 151 Ying-chuan Road, Danshui District, New Taipei City 251, TaiwanDepartment of Electronic Engineering, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, MalaysiaThree-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.http://www.mdpi.com/1999-4893/11/8/122model-based pose estimation3D pose estimationhomography decompositionPnP problemtemplate tracking |
| spellingShingle | Chi-Yi Tsai Kuang-Jui Hsu Humaira Nisar Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms Algorithms model-based pose estimation 3D pose estimation homography decomposition PnP problem template tracking |
| title | Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms |
| title_full | Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms |
| title_fullStr | Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms |
| title_full_unstemmed | Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms |
| title_short | Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms |
| title_sort | efficient model based object pose estimation based on multi template tracking and pnp algorithms |
| topic | model-based pose estimation 3D pose estimation homography decomposition PnP problem template tracking |
| url | http://www.mdpi.com/1999-4893/11/8/122 |
| work_keys_str_mv | AT chiyitsai efficientmodelbasedobjectposeestimationbasedonmultitemplatetrackingandpnpalgorithms AT kuangjuihsu efficientmodelbasedobjectposeestimationbasedonmultitemplatetrackingandpnpalgorithms AT humairanisar efficientmodelbasedobjectposeestimationbasedonmultitemplatetrackingandpnpalgorithms |