LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios
The integration of pedestrian movement analysis with Unmanned Aerial Vehicle (UAV)-based remote sensing enables comprehensive monitoring and a deeper understanding of human dynamics within urban environments, thereby facilitating the optimization of urban planning and public safety strategies. Howev...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/154 |
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| author | Ling Mei Mingyu Fu Bingjie Wang Lvxiang Jia Mingyu Yu Yu Zhang Lijun Zhang |
| author_facet | Ling Mei Mingyu Fu Bingjie Wang Lvxiang Jia Mingyu Yu Yu Zhang Lijun Zhang |
| author_sort | Ling Mei |
| collection | DOAJ |
| description | The integration of pedestrian movement analysis with Unmanned Aerial Vehicle (UAV)-based remote sensing enables comprehensive monitoring and a deeper understanding of human dynamics within urban environments, thereby facilitating the optimization of urban planning and public safety strategies. However, human behavior inherently involves uncertainty, particularly in the prediction of pedestrian trajectories. A major challenge lies in modeling the multimodal nature of these trajectories, including varying paths and targets. Current methods often lack a theoretical framework capable of fully addressing the multimodal uncertainty inherent in trajectory predictions. To tackle this, we propose a novel approach that models uncertainty from two distinct perspectives: (1) the behavioral factor, which reflects historical motion patterns of pedestrians, and (2) the stochastic factor, which accounts for the inherent randomness in future trajectories. To this end, we introduce a global framework named LSN-GTDA, which consists of a pair of symmetrical U-Net networks. This framework symmetrically distributes the semantic segmentation and trajectory prediction modules, enhancing the overall functionality of the network. Additionally, we propose a novel thermal diffusion process, based on signal and system theory, which manages uncertainty by utilizing the full response and providing interpretability to the network. Experimental results demonstrate that the LSN-GTDA method outperforms state-of-the-art approaches on benchmark datasets such as SDD and ETH-UCY, validating its effectiveness in addressing the multimodal uncertainty of pedestrian trajectory prediction. |
| format | Article |
| id | doaj-art-dc308574fb7d4a9d8e985d0a757a2c6b |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dc308574fb7d4a9d8e985d0a757a2c6b2025-01-10T13:20:24ZengMDPI AGRemote Sensing2072-42922025-01-0117115410.3390/rs17010154LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle ScenariosLing Mei0Mingyu Fu1Bingjie Wang2Lvxiang Jia3Mingyu Yu4Yu Zhang5Lijun Zhang6School of Electronic Information, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Electronic Information, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electronic Information, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Electronic Information, Wuhan University of Science and Technology, Wuhan 430081, ChinaDepartment of Electrical and Electronic Engineering, Department of Mechanical and Mechatronics Engineering, Stellenbosch University, Private Bag X1, Matieland 7602, South AfricaThe integration of pedestrian movement analysis with Unmanned Aerial Vehicle (UAV)-based remote sensing enables comprehensive monitoring and a deeper understanding of human dynamics within urban environments, thereby facilitating the optimization of urban planning and public safety strategies. However, human behavior inherently involves uncertainty, particularly in the prediction of pedestrian trajectories. A major challenge lies in modeling the multimodal nature of these trajectories, including varying paths and targets. Current methods often lack a theoretical framework capable of fully addressing the multimodal uncertainty inherent in trajectory predictions. To tackle this, we propose a novel approach that models uncertainty from two distinct perspectives: (1) the behavioral factor, which reflects historical motion patterns of pedestrians, and (2) the stochastic factor, which accounts for the inherent randomness in future trajectories. To this end, we introduce a global framework named LSN-GTDA, which consists of a pair of symmetrical U-Net networks. This framework symmetrically distributes the semantic segmentation and trajectory prediction modules, enhancing the overall functionality of the network. Additionally, we propose a novel thermal diffusion process, based on signal and system theory, which manages uncertainty by utilizing the full response and providing interpretability to the network. Experimental results demonstrate that the LSN-GTDA method outperforms state-of-the-art approaches on benchmark datasets such as SDD and ETH-UCY, validating its effectiveness in addressing the multimodal uncertainty of pedestrian trajectory prediction.https://www.mdpi.com/2072-4292/17/1/154UAVtrajectory predictionglobal thermal diffusioncomplete response analysismulti-modality |
| spellingShingle | Ling Mei Mingyu Fu Bingjie Wang Lvxiang Jia Mingyu Yu Yu Zhang Lijun Zhang LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios Remote Sensing UAV trajectory prediction global thermal diffusion complete response analysis multi-modality |
| title | LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios |
| title_full | LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios |
| title_fullStr | LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios |
| title_full_unstemmed | LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios |
| title_short | LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios |
| title_sort | lsn gtda learning symmetrical network via global thermal diffusion analysis for pedestrian trajectory prediction in unmanned aerial vehicle scenarios |
| topic | UAV trajectory prediction global thermal diffusion complete response analysis multi-modality |
| url | https://www.mdpi.com/2072-4292/17/1/154 |
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