Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels
Moving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tra...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/8153474 |
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| author | Tao Liu Yong Liu |
| author_facet | Tao Liu Yong Liu |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | Moving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. In this paper, we propose an object tracking system using an adaptive ground plane estimation algorithm, facilitated with constrained multiple kernel (CMK) tracking and Kalman filtering, to continuously update the location of moving objects. The proposed algorithm takes advantage of the structure from motion (SfM) to estimate the pose of moving camera, and then the estimated camera’s yaw angle is used as a feedback to improve the accuracy of the ground plane estimation. To further robustly and efficiently tracking objects under occlusion, the constrained multiple kernel tracking technique is adopted in the proposed system to track moving objects in 3D space (depth). The proposed system is evaluated on several challenging datasets, and the experimental results show the favorable performance, which not only can efficiently track on-road objects in a dashcam equipped on a free-moving vehicle but also can well handle occlusion in the tracking. |
| format | Article |
| id | doaj-art-7d5711dc163c4075b30b57d98e71142e |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7d5711dc163c4075b30b57d98e71142e2025-08-20T03:35:19ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/81534748153474Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple KernelsTao Liu0Yong Liu1Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Post and Telecommunications, Beijing, 100876, ChinaBeijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Post and Telecommunications, Beijing, 100876, ChinaMoving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. In this paper, we propose an object tracking system using an adaptive ground plane estimation algorithm, facilitated with constrained multiple kernel (CMK) tracking and Kalman filtering, to continuously update the location of moving objects. The proposed algorithm takes advantage of the structure from motion (SfM) to estimate the pose of moving camera, and then the estimated camera’s yaw angle is used as a feedback to improve the accuracy of the ground plane estimation. To further robustly and efficiently tracking objects under occlusion, the constrained multiple kernel tracking technique is adopted in the proposed system to track moving objects in 3D space (depth). The proposed system is evaluated on several challenging datasets, and the experimental results show the favorable performance, which not only can efficiently track on-road objects in a dashcam equipped on a free-moving vehicle but also can well handle occlusion in the tracking.http://dx.doi.org/10.1155/2021/8153474 |
| spellingShingle | Tao Liu Yong Liu Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels Journal of Advanced Transportation |
| title | Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels |
| title_full | Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels |
| title_fullStr | Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels |
| title_full_unstemmed | Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels |
| title_short | Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels |
| title_sort | moving camera based object tracking using adaptive ground plane estimation and constrained multiple kernels |
| url | http://dx.doi.org/10.1155/2021/8153474 |
| work_keys_str_mv | AT taoliu movingcamerabasedobjecttrackingusingadaptivegroundplaneestimationandconstrainedmultiplekernels AT yongliu movingcamerabasedobjecttrackingusingadaptivegroundplaneestimationandconstrainedmultiplekernels |