Unsupervised Adversarial Learning for Dynamic Background Modeling

2020 ◽  
pp. 248-261
Author(s):  
Maryam Sultana ◽  
Arif Mahmood ◽  
Thierry Bouwmans ◽  
Soon Ki Jung
Keyword(s):  
Background Modeling ◽  
Dynamic Background ◽  
Adversarial Learning

scholarly journals WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2672
Author(s):  
Wenhui Li ◽  
Jianqi Zhang ◽  
Ying Wang
Keyword(s):  
Change Detection ◽  
High Weight ◽  
State Of The Art ◽  
Diffusion Mechanism ◽  
Background Modeling ◽  
Background Model ◽  
Dynamic Background ◽  
Low Weight ◽  
Model Update ◽  
Sample Set

The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS, a weighted pixel-based adaptive segmenter named WePBAS for change detection is proposed in this paper. WePBAS uses weighted background samples as a background model. In the PBAS method, the samples in the background model are not weighted. In the weighted background sample set, the low-weight background samples typically represent the wrong background pixels and need to be replaced. Conversely, high-weight background samples need to be preserved. According to this principle, a directional background model update mechanism is proposed to improve the segmentation performance of the foreground targets in the dynamic background regions. In addition, due to the “background diffusion” mechanism, the PBAS method often identifies small intermittent motion foreground targets as background. To solve this problem, an adaptive foreground counter was added to the WePBAS to limit the “background diffusion” mechanism. The adaptive foreground counter can automatically adjust its own parameters based on videos’ characteristics. The experiments showed that the proposed method is competitive with the state-of-the-art background modeling method for change detection.

Dynamic background modeling using tensor representation and ant colony optimization

2017 ◽  
Vol 60 (11) ◽  
pp. 2287-2302
Author(s):  
LiZhong Peng ◽  
Fan Zhang ◽  
BingYin Zhou
Keyword(s):  
Ant Colony Optimization ◽  
Background Modeling ◽  
Ant Colony ◽  
Tensor Representation ◽  
Dynamic Background

Dynamic background modeling using deep learning autoencoder network

2019 ◽  
Vol 79 (7-8) ◽  
pp. 4639-4659 ◽  
Author(s):  
Jeffin Gracewell ◽  
Mala John
Keyword(s):  
Deep Learning ◽  
Background Modeling ◽  
Dynamic Background

scholarly journals Background Modeling with Extracted Dynamic Pixels for Pumping Unit Surveillance

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Tianming Yu ◽  
Jianhua Yang ◽  
Wei Lu
Keyword(s):  
State Of The Art ◽  
Background Modeling ◽  
Object Motion ◽  
Dynamic Scenes ◽  
Complex Dynamic ◽  
Dynamic Background ◽  
Sample Distribution ◽  
Modeling Methods ◽  
Monitoring Applications ◽  
Pumping Unit

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.

scholarly journals Ant_ViBe: Improved ViBe Algorithm Based on Ant Colony Clustering under Dynamic Background

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yingying Yue ◽  
Dan Xu ◽  
Zhiming Qian ◽  
Hongzhen Shi ◽  
Hao Zhang
Keyword(s):  
Video Processing ◽  
Detection Algorithm ◽  
Background Modeling ◽  
Ant Colony ◽  
Noise Resistance ◽  
Initial Sample ◽  
Dynamic Background ◽  
Evaluation Indexes ◽  
Ant Colony Clustering ◽  
Vibe Algorithm

Foreground target detection algorithm (FTDA) is a fundamental preprocessing step in computer vision and video processing. A universal background subtraction algorithm for video sequences (ViBe) is a fast, simple, efficient and with optimal sample attenuation FTDA based on background modeling. However, the traditional ViBe has three limitations: (1) the noise problem under dynamic background; (2) the ghost problem; and (3) the target adhesion problem. In order to solve the three problems above, ant colony clustering is introduced and Ant_ViBe is proposed in this paper to improve the background modeling mechanism of the traditional ViBe, from the aspects of initial sample modeling, pheromone and ant colony update mechanism, and foreground segmentation criterion. Experimental results show that the Ant_ViBe greatly improved the noise resistance under dynamic background, eased the ghost and targets adhesion problem, and surpassed the typical algorithms and their fusion algorithms in most evaluation indexes.

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