Background Modeling Based on Statistical Clustering Partitioning

Biao Li, Xu Zhiyong, Jianlin Zhang, Xiangru Wang and Xiangsuo Fan

Mathematical Problems in Engineering, 2021, vol. 2021, 1-28

Abstract:

In order to effectively detect dim-small targets in complex scenes, background suppression is applied to highlight the targets. This paper presents a statistical clustering partitioning low-rank background modeling algorithm (SCPLBMA), which clusters the image into several patches based on image statistics. The image matrix of each patch is decomposed into low-rank matrix and sparse matrix in the SCPLBMA. The background of the original video frames is reconstructed from the low-rank matrices, and the targets can be obtained by subtracting the background. Experiments on different scenes show that the SCPLBMA can effectively suppress the background and textures and equalize the residual noise with gray levels significantly lower than that of the targets. Thus, the difference images obtain good stationary characteristics, and the contrast between the targets and the residual backgrounds is significantly improved. Compared with six other algorithms, the SCPLBMA significantly improved the target detection rates of single-frame threshold segmentation.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2021/2346438.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2021/2346438.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2346438

DOI: 10.1155/2021/2346438

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().