Accurate Background Modeling for Moving Object Detection in a Dynamic Scene

2010 ◽  
Author(s):  
Salma Kammoun Jarraya ◽  
Mohamed Hammami ◽  
Hanene Ben-Abdallah
Keyword(s):  
Object Detection ◽  
Background Modeling ◽  
Moving Object Detection ◽  
Moving Object ◽  
Dynamic Scene

Spatiotemporally scalable matrix recovery for background modeling and moving object detection

2020 ◽  
Vol 168 ◽  
pp. 107362 ◽  
Author(s):  
Jingyu Yang ◽  
Wen Shi ◽  
Huanjing Yue ◽  
Kun Li ◽  
Jian Ma ◽  
...  
Keyword(s):  
Object Detection ◽  
Background Modeling ◽  
Moving Object Detection ◽  
Moving Object ◽  
Matrix Recovery

scholarly journals MODFAT: Moving Object Detection by Removing Shadow Based on Fuzzy Technique with an Adaptive Thresholding Method

2021 ◽  
Vol 35 (2) ◽  
pp. 177-183
Author(s):  
Shilpa Mohankumar ◽  
Gopalakrishna Madigondanahalli Thimmaiah ◽  
Naveena Chikkaguddaiah ◽  
Vishruth B. Gowda
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Shadow Removal ◽  
Background Illumination ◽  
Dynamic Scene ◽  
Complex Dynamic ◽  
Illumination Variation ◽  
The Common ◽  
Fuzzy Thresholding

Nowadays, in this technology centric world, gadgets have become handy due to miniaturization. Especially cameras are widely used device for many aspects, one of the common applications is human behavior identification and intelligent video surveillance. In a such application moving object detection in complex dynamic scene is a tedious task due to various challenges such as occlusion, background illumination variation and shadow. Shadows are created in light occlusion in the object it has major impact in accurate object detection. In this paper, object detection with elimination of shadow is addressed. Many existing methods have failed in discriminating the actual moving object from shadow object very accurately. In order to overcome the limitations of existing methods, an improved fuzzy technique rule is used for shadow removal and an adaptive fuzzy thresholding is used for segmenting a foreground object in background. The proposed techniques are experimented with standard and our own datasets and also, it is compared with other existing approaches. Results of proposed method shows improved reliability.

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