A non-parametric pixel-based background modeling for dynamic scenes

Author(s):  
N. Armanfard ◽  
M. Komeili ◽  
M. Valizade ◽  
E. Kabir ◽  
S. Jalili
2011 ◽  
Vol 5 (3) ◽  
pp. 290-299 ◽  
Author(s):  
Jiuyue Hao ◽  
Chao Li ◽  
Zhang Xiong ◽  
Ejaz Hussain

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Fan Xiangsuo ◽  
Xu Zhiyong

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 92329-92340 ◽  
Author(s):  
Wei He ◽  
Yong K-Wan Kim ◽  
Hak-Lim Ko ◽  
Jianhui Wu ◽  
Wujing Li ◽  
...  

2013 ◽  
Vol 25 (5) ◽  
pp. 1101-1103 ◽  
Author(s):  
Thierry Bouwmans ◽  
Jordi Gonzàlez ◽  
Caifeng Shan ◽  
Massimo Piccardi ◽  
Larry Davis

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Tianming Yu ◽  
Jianhua Yang ◽  
Wei Lu

Background modeling plays an important role in the application of intelligent video surveillance. Researchers have presented diverse approaches to support the development of dynamic background modeling. However, in the case of pumping unit surveillance, traditional background modeling methods often mistakenly detect the periodic rotational pumping unit as the foreground object. To address this problem here, we propose a novel background modeling method for foreground segmentation, particularly in dynamic scenes that include a rotational pumping unit. In the proposed method, the ViBe method is employed to extract possible foreground pixels from the sequence frames and then segment the video image into dynamic and static regions. Subsequently, the kernel density estimation (KDE) method is used to build a background model with dynamic samples of each pixel. The bandwidth and threshold of the KDE model are calculated according to the sample distribution and extremum of each dynamic pixel. In addition, the strategy of sample adjustment combines regular and real-time updates. The performance of the proposed method is evaluated against several state-of-the-art methods applied to complex dynamic scenes consisting of a rotational pumping unit. Experimental results show that the proposed method is available for periodic object motion scenario monitoring applications.


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