Research on moving object detection based on improved mixture Gaussian model

Chen, Xiaorong; Xi, Chuanli; Cao, Jianghui

doi:10.1016/j.ijleo.2015.05.122

Cited by 19 publications

(7 citation statements)

References 2 publications

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“…With the subblock based background modeling method and the intensity feature, the difference between the object and the background region can be correctly and quickly recognized. In order to solve the problem caused by the change of the scene, the strategy for Gaussian mixture model updating [ 15 ] is introduced.…”

Section: Object Detection Methodsmentioning

confidence: 99%

A Biological Hierarchical Model Based Underwater Moving Object Detection

Shen

Fan

Tang

et al. 2014

Computational and Mathematical Methods in Medicine

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Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

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Section: Object Detection Methodsmentioning

confidence: 99%

A Biological Hierarchical Model Based Underwater Moving Object Detection

Shen

Fan

Tang

et al. 2014

Computational and Mathematical Methods in Medicine

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“…In the method of background subtraction, a Mixture Gaussian background model (GMM) is applied in traffic extraction. Each pixel in the image follows a Gaussian distribution to deal with fluctuation in pixel value, which has been well developed for real time image tracking, especially in the road traffic (Chen et al, 2015;Lee, 2005). The probability of the current pixel value P( ) in formula ( 1) is measured by the weight of K th Gaussian distribution and the probability density function at the current pixel.…”

Section: Traffic Extractionmentioning

confidence: 99%

A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion

Lee

Zhang

et al. 2017

Computers & Industrial Engineering

117

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An Online Vehicle Routing Problem is a formation of Capacitated Vehicle Routing Problem with re-routing strategy to resolve the problem of inefficient vehicle routing caused by traffic congestion. A flexible delivery rerouting strategy is proposed, which aims at reducing the risk of late delivery. The method of terminating an exploration in a solution by the original ABC algorithm, when the solution is trapped in local optima, is to abandon the solution after specific tolerance limits are set. The phenomenon of local optimal traps will be repeated rapidly after a lengthy recursive process and will eventually result in a low quality solution, with a more complex combinatorial problem when the capability of the exploration is restricted by an inflexible termination criterion.Therefore, this paper proposes a novel scheme using a Multiple Colonies Artificial Bee Colony algorithm. The designs of the outstanding bee selection for colony communication show it to be superior in exploitation. The performance of the proposed algorithm is examined through by Capacitated Vehicle Routing instances and a case study, and the results indicate the potential of using real time information for data-driven vehicle scheduling.

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“…In [17], various background subtraction algorithms based on GMM are comprehensively summarized and compared based on quantitative evaluation indicators to evaluate their detection effects. In [18], a method for initializing the GMM model using the statistical mean and variance is proposed, and the model is updated by the parameter confidence interval. The method has good effect under a static background, but the detection effect is not good under a dynamic background.…”

Section: Introductionmentioning

confidence: 99%

Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

et al. 2019

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In recent years, background subtraction techniques have been used in vision and image applications for moving target detection. However, most methods cannot provide fine results due to dynamic backgrounds, noise, etc. The Gaussian mixture model (GMM) is a background modeling method commonly used in moving target detection. The traditional GMM method is vulnerable to noise interference, especially from dynamic backgrounds; thus, its detection performance is not good. Because of the influence of background noise and dynamic effects on moving target detection, we propose a method of moving target detection for dynamic backgrounds based on improved GMM background subtraction. This method can be divided into three stages. First, in the background modeling stage, to facilitate calculation and improve modeling speed, the video frame is blocked, and the background model is reconstructed using the image block averaging method. Second, in the moving target detection stage, the method of combining wavelet semi-threshold function denoising with mathematical morphology closed operation is used for denoising, which effectively eliminates the influence of noise and improves the detection effect. Third, in the background updating stage, the adaptive background updating method is used to update the background to improve detection results. The simulation results show that the improved method can reduce noise and dynamic background interference while improving moving target detection, thereby proving the effectiveness and adaptability of the proposed method.INDEX TERMS Gaussian mixture model, moving target detection, dynamic background, mathematical morphology, adaptive background updating.

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Research on moving object detection based on improved mixture Gaussian model

Cited by 19 publications

References 2 publications

A Biological Hierarchical Model Based Underwater Moving Object Detection

A Biological Hierarchical Model Based Underwater Moving Object Detection

A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion

Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

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