Adaptively Splitted GMM With Feedback Improvement for the Task of Background Subtraction

Evangelio, Rubén Heras; Pätzold, Michael; Keller, Ivo; Sikora, Thomas

doi:10.1109/tifs.2014.2313919

Cited by 54 publications

(22 citation statements)

References 18 publications

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“…The interval frame difference method is used to replace the common frame difference method, and the time interval between two frames is increased [13]. In a certain extent, the shortcomings of the common frame difference method can be overcome, and the noise of the region is eliminated effectively.…”

Section: Integrate Interval Frame Difference Methodsmentioning

confidence: 99%

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An Improved Gaussian Mixture Model Method for Moving Object Detection

et al. 2016

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Section: Integrate Interval Frame Difference Methodsmentioning

confidence: 99%

“…The ratio of this pixel is bigger, the weight is greater, the contrast is smaller, and the frequency of occurrence is higher. It indicates that the pixel is likely to be background, and the former B distributions are selected as background model of Gaussian distribution [13].…”

mentioning

confidence: 99%

An Improved Gaussian Mixture Model Method for Moving Object Detection

et al. 2016

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“…Evangelio et al (2014a) have estimated the standard deviation of the previous distribution using the median of absolute deviation. In order to make the model parameters and the standard deviation more consistent with the degree of discretization of each data, it is necessary to determine the variation curve of the corrected distance standard difference (σ) with the strength, according to the established change curve, the initial standard deviation for each point is defined as： =̂…”

Section: Adaptive Initialization Of Standard Deviationmentioning

confidence: 99%

Infrared Rotation Plane Radar Change Detection

Wang

Zhang

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The existing change detection method mainly stays on the pixel-level, which is very susceptible to light, shadow, etc. And the complex calculation and analysis for each pixel reduce the detection efficiency. Moreover, there is no modeling determination method to initialize standard deviation of each element for existing mixed Gaussian background modeling methods. In this paper, an improved mixed Gaussian background modeling method is proposed, with the use of infrared rotation plane radar. The relationship between the corrected standard deviation of distance and the detection intensity is used to establish the curve of standard deviation of distance with detected intensity. For each data point, the standard deviation is initialized by the value estimated by the change curve, and the detected distance is used to establish the Gaussian mixture background model. The detection effect of the method is discussed and compared with the traditional Gauss background modeling in the experiment, the result shows that it has certain advantages in processing speed, adaptability to change background and accuracy of change detection.

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“…Therefore an accurate background modeling is significantly critical. In lots of classic algorithm [4,6], using the first N frames for background modeling can build up a precise model via the mathematical statistics analysis of the correlation information and contextual sequences between frames. However, this method fails in extracting dynamic information form the first N frames, especially in satellite video.…”

Section: The Modified Vibe Background Modelmentioning

confidence: 99%

“…The key of this method is to build the background model by adopting the proper methods for pixel sampling. Meanwhile, an appropriate model updating should be carried out according to the segmentation information of the detection of the previous frame to adapt to the change of the background [4]. The classical methods for motion detection target on the processing of the video data on the ground.…”

Section: Introductionmentioning

confidence: 99%

Motion Detection in Satellite Video

Aigong¹,

Wu²,

Zhang³

et al. 2017

J Remote Sensing & GIS

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In view of the problem of satellite video motion detection, a background subtraction method of combining global motion compensation and local dynamic updating is proposed. In the first instance, the improved ViBE model method is used to establish the background model in the middle frame. The background model has one more dynamic update factor. Secondly, the motion model of global scene between frames is estimated by using uniform blocked forward-back LK optical flow, and the global motion compensation is performed. Last but not least, comparison between compensated frame and model, and connected domain analysis are employed to detect and segment the motion objects. Even more, we can correct the update factor of model according to the "pseudo motion" judgment. And then, the model would be updated locally and adaptively. "Target-wise" evaluation recall rate method is proposed which statistic the object entirety but not pixels. We do four experiments using Skysat and JL1H video. The results show that the proposed method perform a favorable effect on "Target-wise" recall rate and the error detection rate is low. The "Target-wise" recall rate is better than 80%. The error detection rate is reduced by at least 10 times, and even more than 160 times, compared with the classical method. The method could be suitable for advanced application and motion analysis in satellite video

show abstract

Adaptively Splitted GMM With Feedback Improvement for the Task of Background Subtraction

Cited by 54 publications

References 18 publications

An Improved Gaussian Mixture Model Method for Moving Object Detection

An Improved Gaussian Mixture Model Method for Moving Object Detection

Infrared Rotation Plane Radar Change Detection

Motion Detection in Satellite Video

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