A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association
Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment,...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/6/959 |
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| author | Anxi Yu Boxu Wei Wenhao Tong Zhihua He Zhen Dong |
| author_facet | Anxi Yu Boxu Wei Wenhao Tong Zhihua He Zhen Dong |
| author_sort | Anxi Yu |
| collection | DOAJ |
| description | Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data. |
| format | Article |
| id | doaj-art-172a9833c00d4dc7a4496eae7300b7d0 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-172a9833c00d4dc7a4496eae7300b7d02025-08-20T03:43:51ZengMDPI AGRemote Sensing2072-42922025-03-0117695910.3390/rs17060959A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data AssociationAnxi Yu0Boxu Wei1Wenhao Tong2Zhihua He3Zhen Dong4College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaVideo Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data.https://www.mdpi.com/2072-4292/17/6/959video synthetic aperture radarmulti-target trackingshadow detectionReID featuresmulti-stage data associationcosine similarity |
| spellingShingle | Anxi Yu Boxu Wei Wenhao Tong Zhihua He Zhen Dong A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association Remote Sensing video synthetic aperture radar multi-target tracking shadow detection ReID features multi-stage data association cosine similarity |
| title | A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association |
| title_full | A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association |
| title_fullStr | A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association |
| title_full_unstemmed | A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association |
| title_short | A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association |
| title_sort | video sar multi target tracking algorithm based on re identification features and multi stage data association |
| topic | video synthetic aperture radar multi-target tracking shadow detection ReID features multi-stage data association cosine similarity |
| url | https://www.mdpi.com/2072-4292/17/6/959 |
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