A Multi-Target Detection Algorithm Using High-Order Differential Equation

Lin, Xiaoyi; Ding, Yipeng; Xu, Xuemei; Sun, Yuning

doi:10.1109/jsen.2019.2901923

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Lin et al have improved the Retina algorithm in the form of a center-surround, which is expected to achieve shadow removal. However, this algorithm needs to iterate on each pixel on the image, determine their filter scales, and compute the anisotropic spread of complex equations after principal component analysis, avoiding the selection of parameter thresholds in the elimination process [16]. Sun et al take the Gaussian model as the basic object according to the practical application needs and incorporate some more typical prior probability parameters more reasonably [17].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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The effect is tested in various specific scenes of sports videos to complete the multitarget motion multitarget tracking detection application applicable to various specific scenes within sports videos. In this paper, deep neural networks are applied to sports video multitarget motion shadow suppression and accurate tracking to improve tracking performance. After the target frame selection is determined, the tracker uses an optical flow method to estimate the limits of the target sports video multitarget motion based on the sports video multitarget motion of the target object between frames. The detector first scans each sports video image frame one by one, observing the previously discovered and learned image frame subregions one by one until the current moment that is highly like the target to be tracked. The preprocessed remote sensing images are converted into grayscale images, the histogram is normalized, and the appropriate height threshold is selected in combination with the regional growth function to realize the rejection of sports video multitarget motion shadow and establish the sports video multitarget network model. The distance and direction of the precise target displacement are determined by frequency-domain vectors and null domain vectors, and the target action judgment mechanism is formed by decision learning. Finally, comparing with other shadow rejection and precision tracking algorithms, the proposed algorithm achieves greater advantages in terms of accuracy and time consumption.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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“…Recent research in human localization has primarily focused on two-dimensional (2D) and three-dimensional (3D) radar signals. To improve the accuracy of Doppler radar target localization, Xiaoyi Lin et al [33] proposed a 2D human localization algorithm based on a high-order differential equation and Doppler processing. In [34], a curve-length method estimating the length of the I/Q signal trajectory was presented, aiming to enhance the sensitivity of phase-based human detection.…”

Section: Human Localizationmentioning

confidence: 99%

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar

Lin,

Hu,

Xie

et al. 2023

Sensors

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Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range–time–Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.

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“…The singular value decomposition (SVD) of the estimated clutter covariance matrices is applied in different radar scenarios [15][16][17], wherein the suppression will be deteriorated in the case of the inconspicuous Bragg peak of sea clutter. One direct extension of the first-order SVD is the high-order SVD [18,19], which exploited the multidimensional structure of measured data to improve the accuracy of the subspace estimation. These methods mainly established Hankel tensors by echo where the target is located.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal projection constant false alarm rate algorithm in fractional domain for small surface targets

Shi

Wang

Tingting

2022

IET Signal Processing

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In this article, a constant false alarm rate (CFAR) detection algorithm based on the fractional Fourier transform (FRFT) and orthogonal projection (OP) is proposed. With a low signal‐to‐clutter ratio (SCR), the radar returns of sea‐surface small targets are often submerged by sea clutter. The FRFT can accumulate target energy in the optimal order. Meanwhile, the OP suppresses the sea clutter in a projection space in the FRFT domain, thus enhancing the output SCR. The FRFT and the OP are in conjunction with the CFAR to realise target detection, which is referred to as the FRFT‐OP‐CFAR. For the FRFT‐OP‐CFAR detector, the most important is the selection of the optimal order p $p$ and the calculation of the OP operator ψx⊥ ${\boldsymbol{\psi }}_{x}^{\perp }$. The experimental results show that the proposed FRFT‐OP‐CFAR has a better clutter suppression and detection performance than some detectors in the authors’ comparison.

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A Multi-Target Detection Algorithm Using High-Order Differential Equation

Cited by 9 publications

References 22 publications

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar

Orthogonal projection constant false alarm rate algorithm in fractional domain for small surface targets

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