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A novel background subtraction algorithm based on parallel vision and Bayesian GANs
DOI:10.1016/j.neucom.2019.04.088.png)
摘要
En 中文
To address the challenges of change detection in the wild, we present a novel background subtraction algorithm based on parallel vision and Bayesian generative adversarial networks (GANs). First, we use the median filtering algorithm for background image extraction. Then, we build the background subtraction model by using Bayesian GANs to classify all pixels into foreground and background, and use parallel vision theory to improve the background subtraction results in complex scenes. The proposed algorithm has been evaluated on the well-known, publicly available changedetection.net dataset. Experiment results show that the proposed algorithm results in better performance than many state-of-the-art ones. In addition, our model trained on CDnet dataset can generalize very well to unseen datasets, outperforming multiple state-of-art methods. The major contribution of this work is to apply parallel vision and Bayesian GANs to solve the difficulties in background subtraction, achieving high detection accuracy. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Background subtraction
Background model
Bayesian generative adversarial network
Convolutional neural networks
Parallel vision
AI总结
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
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Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization场景背景初始化建模方法的广泛基准和调查
Real-time nonparametric background subtraction with tracking-based foreground update
PATTERN RECOGNITION
IF7.6

