| Autor: |
Yang, Xiaqing, Shi, Jun, Chen, Tingjun, Hu, Yao, Zhou, Yuanyuan, Zhang, Xiaoling, Wei, Shunjun, Wu, Junjie |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Geoscience & Remote Sensing; Apr2022, Vol. 60, p1-12, 12p |
| Abstrakt: |
This article extends the shadow tracking for video-synthetic aperture radar (SAR) from a single-target framework to a multitarget framework, which is crucial for SAR ground moving targets’ identification. Inspired by FairMOT, the multitarget tracking framework for SAR shadow tracking is improved by using the triplet attention (TriAtt) mechanism and the lightweight multiscale network. By employing the ability to fuse spatial and feature dimensions of TriAtt and combining the lightweight network optimized by multiscale encoder–decoder and dilated convolution, a fast multiscale feature extraction module (FMsFEM) embedded with TriAtt is proposed for better tracking efficiency and performance. Experiments on the Sandiego video-SAR dataset validate that the TriAtt mechanism can improve the tracking performance of deep layer aggregation (DLA)-34, DLA-18, and FMsFEM significantly. FMsFEM with embedded TriAtt outperforms the state-of-the-art network (FairMOT with backbones of DLA-34 and DLA-18) with much faster frame rates. The average frame rates of FMsFEM and FMsFEM-TriAtt reach 60.32 and 56.13 fps for datasets with an image size of $1088\times 608$ , which are about three times higher than the frame rates of others. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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