Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method
Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as u...
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| Format: | Article |
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MDPI AG
2024-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/18/3380 |
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| author | Zongqing Cao Bing Liu Jianchao Yang Ke Tan Zheng Dai Xingyu Lu Hong Gu |
| author_facet | Zongqing Cao Bing Liu Jianchao Yang Ke Tan Zheng Dai Xingyu Lu Hong Gu |
| author_sort | Zongqing Cao |
| collection | DOAJ |
| description | Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility to noise, and suboptimal performance limits. This study proposes a unified framework to address the challenges of associating interrupted and multi-source track segments by measuring trajectory similarity. We present TSA-cTFER, a novel network utilizing contrastive learning and TransFormer Encoder to accurately assess trajectory similarity through learned Representations by computing distances between high-dimensional feature vectors. Additionally, we tackle dynamic association scenarios with a two-stage online algorithm designed to manage tracks that appear or disappear at any time. This algorithm categorizes track pairs into easy and hard groups, employing tailored association strategies to achieve precise and robust associations in dynamic environments. Experimental results on real-world datasets demonstrate that our proposed TSA-cTFER network with the two-stage online algorithm outperforms existing methods, achieving 94.59% accuracy in interrupted track segment association tasks and 94.83% in multi-source track segment association tasks. |
| format | Article |
| id | doaj-art-d4b46ae3438a441ca30c9e4f2b4d76e9 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d4b46ae3438a441ca30c9e4f2b4d76e92025-08-20T01:55:49ZengMDPI AGRemote Sensing2072-42922024-09-011618338010.3390/rs16183380Contrastive Transformer Network for Track Segment Association with Two-Stage Online MethodZongqing Cao0Bing Liu1Jianchao Yang2Ke Tan3Zheng Dai4Xingyu Lu5Hong Gu6School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaXi’an Electronic Engineering Research Institute, Xi’an 710199, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaInterrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility to noise, and suboptimal performance limits. This study proposes a unified framework to address the challenges of associating interrupted and multi-source track segments by measuring trajectory similarity. We present TSA-cTFER, a novel network utilizing contrastive learning and TransFormer Encoder to accurately assess trajectory similarity through learned Representations by computing distances between high-dimensional feature vectors. Additionally, we tackle dynamic association scenarios with a two-stage online algorithm designed to manage tracks that appear or disappear at any time. This algorithm categorizes track pairs into easy and hard groups, employing tailored association strategies to achieve precise and robust associations in dynamic environments. Experimental results on real-world datasets demonstrate that our proposed TSA-cTFER network with the two-stage online algorithm outperforms existing methods, achieving 94.59% accuracy in interrupted track segment association tasks and 94.83% in multi-source track segment association tasks.https://www.mdpi.com/2072-4292/16/18/3380radar data processingtrack segment associationcontrastive learningtransformer encoderonline association |
| spellingShingle | Zongqing Cao Bing Liu Jianchao Yang Ke Tan Zheng Dai Xingyu Lu Hong Gu Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method Remote Sensing radar data processing track segment association contrastive learning transformer encoder online association |
| title | Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method |
| title_full | Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method |
| title_fullStr | Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method |
| title_full_unstemmed | Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method |
| title_short | Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method |
| title_sort | contrastive transformer network for track segment association with two stage online method |
| topic | radar data processing track segment association contrastive learning transformer encoder online association |
| url | https://www.mdpi.com/2072-4292/16/18/3380 |
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