Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement
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Abstract (arXiv)
Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy. Thus, a shadow-background-noise 3D spatial decomposition (SBN-3D-SD) model is proposed to enhance shadows for higher detection-tracking accuracy. It leverages the sparse property of shadows, the low-rank property of back-grounds, and the Gaussian property of noises to perform 3D spatial three-decomposition. It separates shadows from back-grounds and noises by the alternating direction method of multi-pliers (ADMM). Results on the Sandia National Laboratories (SNL) data verify its effectiveness. It boosts the shadow saliency from the qualitative and quantitative evaluation. It boosts the shadow detection accuracy of Faster R-CNN, RetinaNet and YOLOv3. It also boosts the shadow tracking accuracy of TransTrack, FairMOT and ByteTrack.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 7 Jul 2022.
Builds on
6 of 28 references.
References · 6
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2015Fast R-CNNcited by 2,930
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
- 2020FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Trackingcited by 489
- 2020TransTrack: Multiple Object Tracking with Transformercited by 370
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Citing papers per year: 2022: 1.
1 paper.