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基于显著度引导的两级粒子滤波的低信噪比红外点目标检测与跟踪。

Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter.

机构信息

Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.

Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200083, China.

出版信息

Sensors (Basel). 2022 Apr 5;22(7):2791. doi: 10.3390/s22072791.

DOI:10.3390/s22072791
PMID:35408405
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9003241/
Abstract

Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods.

摘要

低信噪比 (SNR) 红外点目标检测与跟踪对于红外遥感研究至关重要。在低 SNR 图像中，强烈的噪声会淹没目标。在这封信中，提出了一种由搜索粒子滤波器 (PF) 和跟踪 PF 组成的显著引导双阶段粒子滤波器 (SGDS-PF)，用于检测和跟踪目标。在搜索 PF 之前，引入了单帧和多帧目标积累方法，以抑制噪声并增强目标。此外，提出了似然估计滤波器和图像块分割，以提取似然显著度并获得适当的建议密度。搜索 PF 以该建议密度为指导，有效地检测潜在目标。然后，利用搜索 PF 的结果，采用跟踪 PF 跟踪和确认潜在目标。最后，将输出真实目标的轨迹。与现有方法相比，SGDS-PF 优化了低 SNR 图像的建议密度。搜索 PF 使用少量精确的粒子快速准确地检测潜在目标。此外，搜索 PF 初始化后，即使在强噪声下，跟踪 PF 也可以使用很少的粒子继续跟踪目标。此外，通过实验适当选择了参数。广泛的实验结果表明，SGDS-PF 在跟踪精度、跟踪可靠性和时间消耗方面具有出色的性能。SGDS-PF 优于其他先进方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/71f4a27cb9a4/sensors-22-02791-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/6352c29d1fcd/sensors-22-02791-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/4972d32a595e/sensors-22-02791-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/6e7575309790/sensors-22-02791-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/202f3e009583/sensors-22-02791-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/3f0c779b0935/sensors-22-02791-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/34ff2757c004/sensors-22-02791-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/271e715f1d78/sensors-22-02791-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/c3f168ed3f09/sensors-22-02791-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/49847a48faeb/sensors-22-02791-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/f93aad38af74/sensors-22-02791-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/aba0dd436350/sensors-22-02791-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/d696185db01a/sensors-22-02791-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/e5093444512f/sensors-22-02791-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/71f4a27cb9a4/sensors-22-02791-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/6352c29d1fcd/sensors-22-02791-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/4972d32a595e/sensors-22-02791-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/6e7575309790/sensors-22-02791-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/202f3e009583/sensors-22-02791-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/3f0c779b0935/sensors-22-02791-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/34ff2757c004/sensors-22-02791-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/271e715f1d78/sensors-22-02791-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/c3f168ed3f09/sensors-22-02791-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/49847a48faeb/sensors-22-02791-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/f93aad38af74/sensors-22-02791-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/aba0dd436350/sensors-22-02791-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/d696185db01a/sensors-22-02791-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/e5093444512f/sensors-22-02791-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5419/9003241/71f4a27cb9a4/sensors-22-02791-g015.jpg

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