Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD
Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude envir...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/5/341 |
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| author | Chenghang Wang Xiaochun Shen Zhaoxiang Zhang Chengyang Tao Yuelei Xu |
| author_facet | Chenghang Wang Xiaochun Shen Zhaoxiang Zhang Chengyang Tao Yuelei Xu |
| author_sort | Chenghang Wang |
| collection | DOAJ |
| description | Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude environments, small target proportions, irregular target movement, and frequent occlusions complicate the multi-object tracking task. This paper proposes a cross-scene multi-object tracking (CST) method to address these challenges. Firstly, a lightweight object detection framework is proposed to optimize key sub-tasks by integrating multi-dimensional temporal and spatial information. Secondly, trajectory prediction is achieved through the implementation of Model-Agnostic Meta-Learning, enhancing adaptability to dynamic environments. Thirdly, re-identification is facilitated using Dempster–Shafer Theory, which effectively manages uncertainties in target recognition by incorporating aircraft state information. Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields. |
| format | Article |
| id | doaj-art-4e3f168e584548cf9da36e4eb06e6b33 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-4e3f168e584548cf9da36e4eb06e6b332025-08-20T03:47:53ZengMDPI AGDrones2504-446X2025-04-019534110.3390/drones9050341Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTDChenghang Wang0Xiaochun Shen1Zhaoxiang Zhang2Chengyang Tao3Yuelei Xu4Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaMulti-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude environments, small target proportions, irregular target movement, and frequent occlusions complicate the multi-object tracking task. This paper proposes a cross-scene multi-object tracking (CST) method to address these challenges. Firstly, a lightweight object detection framework is proposed to optimize key sub-tasks by integrating multi-dimensional temporal and spatial information. Secondly, trajectory prediction is achieved through the implementation of Model-Agnostic Meta-Learning, enhancing adaptability to dynamic environments. Thirdly, re-identification is facilitated using Dempster–Shafer Theory, which effectively manages uncertainties in target recognition by incorporating aircraft state information. Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields.https://www.mdpi.com/2504-446X/9/5/341UAVmulti-object trackingtrajectory predictionre-identification |
| spellingShingle | Chenghang Wang Xiaochun Shen Zhaoxiang Zhang Chengyang Tao Yuelei Xu Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD Drones UAV multi-object tracking trajectory prediction re-identification |
| title | Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD |
| title_full | Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD |
| title_fullStr | Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD |
| title_full_unstemmed | Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD |
| title_short | Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD |
| title_sort | cross scene multi object tracking for drones leveraging meta learning and onboard parameters with the new middtd |
| topic | UAV multi-object tracking trajectory prediction re-identification |
| url | https://www.mdpi.com/2504-446X/9/5/341 |
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