Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration
In traffic scenarios, vehicle trajectories can provide almost all the dynamic information of moving vehicles. Analyzing the vehicle trajectory in the monitoring scene can grasp the dynamic road traffic information. Cross-camera association of vehicle trajectories in multiple cameras can break the is...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/5006347 |
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| _version_ | 1832561271114825728 |
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| author | Junfang Song Yao Fan Huansheng Song Haili Zhao |
| author_facet | Junfang Song Yao Fan Huansheng Song Haili Zhao |
| author_sort | Junfang Song |
| collection | DOAJ |
| description | In traffic scenarios, vehicle trajectories can provide almost all the dynamic information of moving vehicles. Analyzing the vehicle trajectory in the monitoring scene can grasp the dynamic road traffic information. Cross-camera association of vehicle trajectories in multiple cameras can break the isolation of target information between single cameras and obtain the overall road operation conditions in a large-scale video surveillance area, which helps road traffic managers to conduct traffic analysis, prediction, and control. Based on the framework of DBT automatic target detection, this paper proposes a cross-camera vehicle trajectory correlation matching method based on the Euclidean distance metric correlation of trajectory points. For the multitarget vehicle trajectory acquired in a single camera, we first perform 3D trajectory reconstruction based on the combined camera calibration in the overlapping area and then complete the similarity association between the cross-camera trajectories and the cross-camera trajectory update, and complete the trajectory transfer of the vehicle between adjacent cameras. Experiments show that the method in this paper can well solve the problem that the current tracking technology is difficult to match the vehicle trajectory under different cameras in complex traffic scenes and essentially achieves long-term and long-distance continuous tracking and trajectory acquisition of multiple targets across cameras. |
| format | Article |
| id | doaj-art-23d15ace3fea46aebee16addd360954f |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-23d15ace3fea46aebee16addd360954f2025-02-03T01:25:21ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/5006347Target Tracking and 3D Trajectory Reconstruction Based on Multicamera CalibrationJunfang Song0Yao Fan1Huansheng Song2Haili Zhao3School of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringIn traffic scenarios, vehicle trajectories can provide almost all the dynamic information of moving vehicles. Analyzing the vehicle trajectory in the monitoring scene can grasp the dynamic road traffic information. Cross-camera association of vehicle trajectories in multiple cameras can break the isolation of target information between single cameras and obtain the overall road operation conditions in a large-scale video surveillance area, which helps road traffic managers to conduct traffic analysis, prediction, and control. Based on the framework of DBT automatic target detection, this paper proposes a cross-camera vehicle trajectory correlation matching method based on the Euclidean distance metric correlation of trajectory points. For the multitarget vehicle trajectory acquired in a single camera, we first perform 3D trajectory reconstruction based on the combined camera calibration in the overlapping area and then complete the similarity association between the cross-camera trajectories and the cross-camera trajectory update, and complete the trajectory transfer of the vehicle between adjacent cameras. Experiments show that the method in this paper can well solve the problem that the current tracking technology is difficult to match the vehicle trajectory under different cameras in complex traffic scenes and essentially achieves long-term and long-distance continuous tracking and trajectory acquisition of multiple targets across cameras.http://dx.doi.org/10.1155/2022/5006347 |
| spellingShingle | Junfang Song Yao Fan Huansheng Song Haili Zhao Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration Journal of Advanced Transportation |
| title | Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration |
| title_full | Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration |
| title_fullStr | Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration |
| title_full_unstemmed | Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration |
| title_short | Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration |
| title_sort | target tracking and 3d trajectory reconstruction based on multicamera calibration |
| url | http://dx.doi.org/10.1155/2022/5006347 |
| work_keys_str_mv | AT junfangsong targettrackingand3dtrajectoryreconstructionbasedonmulticameracalibration AT yaofan targettrackingand3dtrajectoryreconstructionbasedonmulticameracalibration AT huanshengsong targettrackingand3dtrajectoryreconstructionbasedonmulticameracalibration AT hailizhao targettrackingand3dtrajectoryreconstructionbasedonmulticameracalibration |