Robust Shadow Tracking for Video SAR

Zhao, Baojun; Han, Yan; Wang, Hongshuo; Tang, Linbo; Liu, Xun; Wang, Taoyang

doi:10.1109/lgrs.2020.2988165

Cited by 42 publications

(19 citation statements)

References 24 publications

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“…d P and ar F are defined as follows: The probability of detection P d and false alarm rate F ar were used to evaluate the detection performance of the proposed method. P d and F ar are defined as follows: 14) where N c denotes the number of correct instances of detection and N r denotes the number of real moving targets. N f represents the number of false alarms and N all represents the number of all detected targets.…”

Section: Results Of Shadow Detectionmentioning

confidence: 99%

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Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

Chen

et al. 2021

Remote Sensing

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Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow.

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Section: Results Of Shadow Detectionmentioning

confidence: 99%

“…The shadow of a moving target can be used as an object of detection in VideoSAR [12][13][14][15]. This has the theoretical advantages of a high positioning accuracy, high rate of detection, and low minimum detectable velocity.…”

Section: Introductionmentioning

confidence: 99%

Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

Chen

et al. 2021

Remote Sensing

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“…Unlike the RGB three-channel optical images, the SAR images are gray; therefore, all three channels are assigned to the same gray value to use the pre-trained weights. Applying models trained on three-channel RGB images to one-channel radar images has been carried out in several published literatures [10,12,15], and the results in Section 5.3 show that it is reasonable to do so.…”

Section: Tracking Processmentioning

confidence: 99%

“…Therefore, shadow-aided moving target tracking has become a hot topic in Video-SAR. In recent years, many scholars have worked on shadow-aided moving target tracking in Video-SAR [9][10][11]. Wang et al [9] fully considered the constant grayscale of shadows and used data multiplexing to achieve moving target tracking.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [9] fully considered the constant grayscale of shadows and used data multiplexing to achieve moving target tracking. Zhao et al [10] applied the saliency-based detection mechanism and used spatial-temporal information to achieve moving target tracking in Video-SAR. Tian et al [11] utilized the dynamic programming-based particle filter to achieve the track-before-detect algorithm in Video-SAR.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Guided Anchor Siamese Network for Arbitrary Target-of-Interest Tracking in Video-SAR

et al. 2021

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Video synthetic aperture radar (Video-SAR) allows continuous and intuitive observation and is widely used for radar moving target tracking. The shadow of a moving target has the characteristics of stable scattering and no location shift, making moving target tracking using shadows a hot topic. However, the existing techniques mainly rely on the appearance of targets, which is impractical and costly, especially for tracking targets of interest (TOIs) with high diversity and arbitrariness. Therefore, to solve this problem, we propose a novel guided anchor Siamese network (GASN) dedicated to arbitrary TOI tracking in Video-SAR. First, GASN searches for matching areas in the subsequent frames with the initial area of the TOI in the first frame are conducted, returning the most similar area using a matching function, which is learned from general training without TOI-related data. With the learned matching function, GASN can be used to track arbitrary TOIs. Moreover, we also constructed a guided anchor subnetwork, referred to as GA-SubNet, which employs the prior information of the first frame and generates sparse anchors of the same shape as the TOIs. The number of unnecessary anchors is therefore reduced to suppress false alarms. Our method was evaluated on simulated and real Video-SAR data. The experimental results demonstrated that GASN outperforms state-of-the-art methods, including two types of traditional tracking methods (MOSSE and KCF) and two types of modern deep learning techniques (Siamese-FC and Siamese-RPN). We also conducted an ablation experiment to demonstrate the effectiveness of GA-SubNet.

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Real-Time Vehicle Detection Method Based on Aerial Image in Complex Background

Liang

et al. 2022

Lecture Notes in Electrical Engineering

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Robust Shadow Tracking for Video SAR

Cited by 42 publications

References 24 publications

Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

A Novel Guided Anchor Siamese Network for Arbitrary Target-of-Interest Tracking in Video-SAR

Real-Time Vehicle Detection Method Based on Aerial Image in Complex Background

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