Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data
The monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by the...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/11340 |
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| author | Yang Chen Xin Chen Bin Bai Linjiang Zheng |
| author_facet | Yang Chen Xin Chen Bin Bai Linjiang Zheng |
| author_sort | Yang Chen |
| collection | DOAJ |
| description | The monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by the insufficient density of monitoring points, significantly reduce their usability. Therefore, it is important to reconstruct the target trajectories. Existing methods for the reconstruction of target trajectories often rely on topological data and convert trajectory reconstruction into a trajectory matching problem. Such methods heavily rely on topological data and cannot reconstruct trajectories in free space. To address this issue, we proposed a trajectory reconstruction method, named Prob-Attn, which does not rely on topological data and can accurately reconstruct target trajectories in free space. This method can be divided into two steps: first, a spatial trajectory construction module is proposed to determine the spatial trajectories of targets. Then, based on the reconstructed spatial trajectory of the target, this paper proposes a time series prediction model based on historical trajectories and an attention mechanism, which considers the impact of the target’s activity cycle and the surrounding status to predict the time series inside the trajectory. Finally, the proposed method is evaluated on real automatic vehicle detection datasets collected in Chongqing, China. The experimental results show that, compared with traditional methods, the proposed method can reconstruct the spatiotemporal trajectory of the target more accurately. The reconstructed trajectory data can be used for critical applications such as the intent and behavior analysis of key targets in national airspace and ground areas, providing valuable insights into security and safety. |
| format | Article |
| id | doaj-art-c881fd09e98347daa65f7d44b6986bfe |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c881fd09e98347daa65f7d44b6986bfe2024-12-13T16:23:36ZengMDPI AGApplied Sciences2076-34172024-12-0114231134010.3390/app142311340Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection DataYang Chen0Xin Chen1Bin Bai2Linjiang Zheng3Institute of Southwestern Communication, Chengdu 610041, ChinaCollege of Computer Science, Chongqing University, Chongqin 400044, ChinaInstitute of Southwestern Communication, Chengdu 610041, ChinaCollege of Computer Science, Chongqing University, Chongqin 400044, ChinaThe monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by the insufficient density of monitoring points, significantly reduce their usability. Therefore, it is important to reconstruct the target trajectories. Existing methods for the reconstruction of target trajectories often rely on topological data and convert trajectory reconstruction into a trajectory matching problem. Such methods heavily rely on topological data and cannot reconstruct trajectories in free space. To address this issue, we proposed a trajectory reconstruction method, named Prob-Attn, which does not rely on topological data and can accurately reconstruct target trajectories in free space. This method can be divided into two steps: first, a spatial trajectory construction module is proposed to determine the spatial trajectories of targets. Then, based on the reconstructed spatial trajectory of the target, this paper proposes a time series prediction model based on historical trajectories and an attention mechanism, which considers the impact of the target’s activity cycle and the surrounding status to predict the time series inside the trajectory. Finally, the proposed method is evaluated on real automatic vehicle detection datasets collected in Chongqing, China. The experimental results show that, compared with traditional methods, the proposed method can reconstruct the spatiotemporal trajectory of the target more accurately. The reconstructed trajectory data can be used for critical applications such as the intent and behavior analysis of key targets in national airspace and ground areas, providing valuable insights into security and safety.https://www.mdpi.com/2076-3417/14/23/11340target trajectory reconstructionautomatic vehicle detectionprobability modelattention network |
| spellingShingle | Yang Chen Xin Chen Bin Bai Linjiang Zheng Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data Applied Sciences target trajectory reconstruction automatic vehicle detection probability model attention network |
| title | Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data |
| title_full | Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data |
| title_fullStr | Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data |
| title_full_unstemmed | Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data |
| title_short | Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data |
| title_sort | spatial temporal reconstruction of trajectories in free space using automatic target position detection data |
| topic | target trajectory reconstruction automatic vehicle detection probability model attention network |
| url | https://www.mdpi.com/2076-3417/14/23/11340 |
| work_keys_str_mv | AT yangchen spatialtemporalreconstructionoftrajectoriesinfreespaceusingautomatictargetpositiondetectiondata AT xinchen spatialtemporalreconstructionoftrajectoriesinfreespaceusingautomatictargetpositiondetectiondata AT binbai spatialtemporalreconstructionoftrajectoriesinfreespaceusingautomatictargetpositiondetectiondata AT linjiangzheng spatialtemporalreconstructionoftrajectoriesinfreespaceusingautomatictargetpositiondetectiondata |