Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices
Real-time object pose estimation and tracking is challenging but essential for some emerging applications, such as augmented reality. In general, state-of-the-art methods address this problem using deep neural networks, which indeed yield satisfactory results. Nevertheless, the high computational co...
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| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Indoor and Seamless Positioning and Navigation |
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| Online Access: | https://ieeexplore.ieee.org/document/10352604/ |
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| author | Yo-Chung Lau Kuan-Wei Tseng Peng-Yuan Kao I-Ju Hsieh Hsiao-Ching Tseng Yi-Ping Hung |
| author_facet | Yo-Chung Lau Kuan-Wei Tseng Peng-Yuan Kao I-Ju Hsieh Hsiao-Ching Tseng Yi-Ping Hung |
| author_sort | Yo-Chung Lau |
| collection | DOAJ |
| description | Real-time object pose estimation and tracking is challenging but essential for some emerging applications, such as augmented reality. In general, state-of-the-art methods address this problem using deep neural networks, which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them unsuitable for mobile devices where real-world applications usually take place. We propose real-time object pose tracking system with low computational cost for mobile devices. It is a monocular inertial-assisted-visual system with a client–server architecture connected by high-speed networking. Inertial measurement unit (IMU) pose propagation is performed on the client side for fast pose tracking, and RGB image-based 3-D object pose estimation is performed on the server side to obtain accurate poses, after which the pose is sent to the client side for refinement, where we propose a bias self-correction mechanism to reduce the drift. We also propose a fast and effective pose inspection algorithm to detect tracking failures and incorrect pose estimation. In this way, the pose updates rapidly even within 5 ms on low-level devices, making it possible to support real-time tracking for applications. In addition, an object pose dataset with RGB images and IMU measurements is delivered for evaluation. Experiments also show that our method performs well with both accuracy and robustness. |
| format | Article |
| id | doaj-art-66d31d4dcbd94cd3889efdf0fb4ad04b |
| institution | DOAJ |
| issn | 2832-7322 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Indoor and Seamless Positioning and Navigation |
| spelling | doaj-art-66d31d4dcbd94cd3889efdf0fb4ad04b2025-08-20T02:53:07ZengIEEEIEEE Journal of Indoor and Seamless Positioning and Navigation2832-73222023-01-01121122010.1109/JISPIN.2023.334098710352604Real-Time Object Pose Tracking System With Low Computational Cost for Mobile DevicesYo-Chung Lau0https://orcid.org/0009-0005-8251-1270Kuan-Wei Tseng1https://orcid.org/0000-0003-1134-5314Peng-Yuan Kao2https://orcid.org/0000-0002-5582-1039I-Ju Hsieh3https://orcid.org/0009-0002-0561-5154Hsiao-Ching Tseng4https://orcid.org/0009-0006-1590-8072Yi-Ping Hung5https://orcid.org/0000-0002-9373-2184Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, TaiwanDepartment of Computer Science, Tokyo Institute of Technology, Tokyo, JapanGraduate Institute of Networking and Multimedia, National Taiwan University, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanGraduate Institute of Networking and Multimedia, National Taiwan University, Taipei, TaiwanReal-time object pose estimation and tracking is challenging but essential for some emerging applications, such as augmented reality. In general, state-of-the-art methods address this problem using deep neural networks, which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them unsuitable for mobile devices where real-world applications usually take place. We propose real-time object pose tracking system with low computational cost for mobile devices. It is a monocular inertial-assisted-visual system with a client–server architecture connected by high-speed networking. Inertial measurement unit (IMU) pose propagation is performed on the client side for fast pose tracking, and RGB image-based 3-D object pose estimation is performed on the server side to obtain accurate poses, after which the pose is sent to the client side for refinement, where we propose a bias self-correction mechanism to reduce the drift. We also propose a fast and effective pose inspection algorithm to detect tracking failures and incorrect pose estimation. In this way, the pose updates rapidly even within 5 ms on low-level devices, making it possible to support real-time tracking for applications. In addition, an object pose dataset with RGB images and IMU measurements is delivered for evaluation. Experiments also show that our method performs well with both accuracy and robustness.https://ieeexplore.ieee.org/document/10352604/Low computational costmobile deviceobject pose estimationreal-time object pose tracking |
| spellingShingle | Yo-Chung Lau Kuan-Wei Tseng Peng-Yuan Kao I-Ju Hsieh Hsiao-Ching Tseng Yi-Ping Hung Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices IEEE Journal of Indoor and Seamless Positioning and Navigation Low computational cost mobile device object pose estimation real-time object pose tracking |
| title | Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices |
| title_full | Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices |
| title_fullStr | Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices |
| title_full_unstemmed | Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices |
| title_short | Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices |
| title_sort | real time object pose tracking system with low computational cost for mobile devices |
| topic | Low computational cost mobile device object pose estimation real-time object pose tracking |
| url | https://ieeexplore.ieee.org/document/10352604/ |
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