Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter Article Swipe
Liangjie Jia
,
Peng Rao
,
Yuke Zhang
,
Yueqi Su
,
Xin Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22072791
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22072791
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.
Related Topics
Concepts
Tracking (education)
Computer vision
Artificial intelligence
Computer science
Particle filter
Segmentation
Noise (video)
Filter (signal processing)
Frame (networking)
Point target
Point (geometry)
Pattern recognition (psychology)
Mathematics
Image (mathematics)
Telecommunications
Synthetic aperture radar
Geometry
Pedagogy
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22072791
- https://www.mdpi.com/1424-8220/22/7/2791/pdf?version=1649347163
- OA Status
- gold
- Cited By
- 14
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4223593794
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4223593794Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22072791Digital Object Identifier
- Title
-
Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle FilterWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-05Full publication date if available
- Authors
-
Liangjie Jia, Peng Rao, Yuke Zhang, Yueqi Su, Xin ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/s22072791Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/7/2791/pdf?version=1649347163Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/7/2791/pdf?version=1649347163Direct OA link when available
- Concepts
-
Tracking (education), Computer vision, Artificial intelligence, Computer science, Particle filter, Segmentation, Noise (video), Filter (signal processing), Frame (networking), Point target, Point (geometry), Pattern recognition (psychology), Mathematics, Image (mathematics), Telecommunications, Synthetic aperture radar, Geometry, Pedagogy, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3, 2023: 5, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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