Fast convergence of regularised Region-based Mixture of Gaussians for dynamic background modelling

Varadarajan, Sriram; Wang, Hongbin; Miller, Paul; Zhou, Huiyu

doi:10.1016/j.cviu.2014.12.004

Cited by 17 publications

(3 citation statements)

References 42 publications

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“…precision = #correctlyclassifiedforegroundpixels #pixelsclassifiedasforeground (10) F−measure = 2 recall × precision recall + precision (11)…”

Section: Experiments Datasets and Evaluation Metricsmentioning

confidence: 99%

“…Varadarajan et al [9] put forward a dynamic background subtraction method combining neighbouring pixels in GMM framework, which can cope with complex dynamic scenes effectively. Due to the adoption of several Gaussian models, GMM‐based method [9, 10] is suitable for complex scenes such as swaying branches, water surface fluctuation and so on. Notably, these methods are relatively complex and there is a need for many parameters to be stored, with the subsequent large amount of computation.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic background subtraction method based on spatio‐temporal classification

Jiang

2018

IET Computer Vision

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The dynamic background will cause extremely negative effects on background subtraction and is difficult to eliminate. This study proposes a dynamic background subtraction method based on a spatio-temporal classification which mainly contains two key steps: temporal and spatial classifications. For temporal classification, the closest pixel sampling algorithm is used to sample background pixels in groups, which avoids centralised sampling and a complicated mathematical modelling process. For the background model obtained by group sampling, the pixels which are similar to the detected pixel are classified into the same category. According to the number of pixels in this category, the label (foreground or background) of the detected pixel can be determined thus a coarse foreground mask is obtained. For spatial classification, considering the correlation between dynamic background pixels and neighbouring pixels, a square window can be set for each foreground pixel in the coarse mask, and then all pixels in the window classified. According to the labels of these classified pixels, a more accurate foreground mask is obtained. The experiments on public datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

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“…precision = #correctlyclassifiedforegroundpixels #pixelsclassifiedasforeground (10) F−measure = 2 recall × precision recall + precision (11)…”

Section: Experiments Datasets and Evaluation Metricsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic background subtraction method based on spatio‐temporal classification

Jiang

2018

IET Computer Vision

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“…Therefore, effective algorithms that can isolate moving objects in video sequences are urgently needed. Recently, foreground detection methods in dynamic scenes based on mixture of Gaussians modelling have been proposed (Varadaraja et al, 2015a;Varadaraja et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

Foreground Segmentation for Moving Cameras under Low Illumination Conditions

Wang

Yin

et al. 2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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A foreground segmentation method, including image enhancement, trajectory classification and object segmentation, is proposed for moving cameras under low illumination conditions. Gradient-field-based image enhancement is designed to enhance low-contrast images. On the basis of the dense point trajectories obtained in long frames sequences, a simple and effective clustering algorithm is designed to classify foreground and background trajectories. By combining trajectory points and a marker-controlled watershed algorithm, a new type of foreground labeling algorithm is proposed to effectively reduce computing costs and improve edge-preserving performance. Experimental results demonstrate the promising performance of the proposed approach compared with other competing methods.

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