A Weak Moving Point Target Detection Method Based on High Frame Rate Image Sequences

Wu, Yong; Zheng, Yang; Niu, Wenlong; Zheng, Wei

doi:10.1109/igarss.2018.8518035

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Recently, Liu et al used FFT and KL to calculate the similarity between the TP and waveform to detect the target signal [37]. Niu et al proposed detection methods based on statistical distribution distance involving high-frame-rate detection [38][39][40]. These methods are effective for TPs with a high SNR, but for TPs with a low SNR, the target signal cannot be separated from the background signal, and the time when the target appears in the TP cannot be identified.…”

Section: Tp-based Detection Methodsmentioning

confidence: 99%

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Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

et al. 2023

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Moving target detection in optical remote sensing is important for satellite surveillance and space target monitoring. Here, a new moving point target detection framework under a low signal-to-noise ratio (SNR) that uses an end-to-end network (1D-ResNet) to learn the distribution features of transient disturbances in the temporal profile (TP) formed by a target passing through a pixel is proposed. First, we converted the detection of the point target in the image into the detection of transient disturbance in the TP and established mathematical models of different TP types. Then, according to the established mathematical models of TP, we generated the simulation TP dataset to train the 1D-ResNet. In 1D-ResNet, the structure of CBR-1D (Conv1D, BatchNormalization, ReLU) was designed to extract the features of transient disturbance. As the transient disturbance is very weak, we used several skip connections to prevent the loss of features in the deep layers. After the backbone, two LBR (Linear, BatchNormalization, ReLU) modules were used for further feature extraction to classify TP and identify the locations of transient disturbances. A multitask weighted loss function to ensure training convergence was proposed. Sufficient experiments showed that this method effectively detects moving point targets with a low SNR and has the highest detection rate and the lowest false alarm rate compared to other benchmark methods. Our method also has the best detection efficiency.

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Section: Tp-based Detection Methodsmentioning

confidence: 99%

“…To verify the performance of the proposed method, we compared it with some benchmark methods, including MaxMean [7], IPI [13], LCM [8], Kernel [38], ICLSP [35], NAF [36], and TRLCM [44]. MaxMean, IPI, and LCM are spatial-based methods, whereas Kernel, ICLSP, NAF, and TRLCM are temporal-based methods.…”

Section: Contrast Experiments With the Benchmark Methodsmentioning

confidence: 99%

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

et al. 2023

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“…The target is detected by combining the priors of the target or depending on the differences between the temporal gray distributions directly. Wu et al [21] detected a small infrared target by analyzing the correlation between temporal profiles using a kernel algorithm. In [22], researchers detected the small target robustly by combining the movement and appearance cues.…”

Section: ) Single Pixel Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in this paper. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed (https://small-infrared-aerial-target-detection.grand-challenge.or g/). To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.

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Moving scene object tracking method based on deep convolutional neural network

Liu,

Lin,

Yang

2024

Alexandria Engineering Journal

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A Weak Moving Point Target Detection Method Based on High Frame Rate Image Sequences

Cited by 9 publications

References 9 publications

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Moving scene object tracking method based on deep convolutional neural network

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