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18 December 2019 Dim small targets detection based on statistical block low-rank background modeling
Biao Li, Zhiyong Xu, Jianlin Zhang, Xiangsuo Fan
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Proceedings Volume 11338, AOPC 2019: Optical Sensing and Imaging Technology; 113382J (2019) https://doi.org/10.1117/12.2547630
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China
Abstract
How to effectively detect weak targets from complex background is always a challenging problem and is a meaningful research subject with practical significance. In this paper, the complex video frame images are considered as a spatial random process, and the stationarity and low-rank characteristics of different components of the image are related to theirs statistical characteristics. According to this view, a statistical block low-rank background modeling algorithm (for short: SBLR) is proposed. This paper first analyzes the regional statistical characteristics of the image, and then uses k-mean statistical clustering algorithm to divide the image into statistical blocks to obtain the statistical block images. Then, the characteristics of each component of the statistical block image are analyzed to establish a model composed of statistical block low rank background and sparse components. Next, according to the characteristics of each component of the model, the solution scheme of principal component analysis is adopted, and the specific solution algorithm is given. Finally, the background reconstruction experiment according to SBLR algorithm and target detection experiment are carried out. Experiments show that the algorithm proposed in this paper achieves good accuracy in the background reconstruction of complex scenes, the background is significantly suppressed, the target is significantly enhanced, and the target detection rate is high.
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Biao Li, Zhiyong Xu, Jianlin Zhang, and Xiangsuo Fan "Dim small targets detection based on statistical block low-rank background modeling", Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113382J (18 December 2019); https://doi.org/10.1117/12.2547630
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KEYWORDS
Target detection
Detection and tracking algorithms
Digital filtering
Signal to noise ratio
Reconstruction algorithms
Statistical modeling
Image processing
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