An adaptive background modeling based on modified running Gaussian average method

Rahman, Md. Asifur; Ahmed, Boshir; Hossian, Md. Ali; Mondal, Md. Nazrul İslam

doi:10.1109/ecace.2017.7912961

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…e simplest parametric background modeling method is based on a statistical analysis of the histogram values for each pixel of the past K frames, and the mean and median or the maximum frequency is used to estimate the background [7]. Another parametric background method is to establish a single-peak probability density function for background pixels, such as running Gaussian average [8]. Since a single Gaussian density function cannot handle dynamic background scenarios, the Gaussian mixture model (GMM) composed of N Gaussian component is established for each pixel to estimate the background [9].…”

Section: Introductionmentioning

confidence: 99%

Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model

Cao

Wang

Long

2021

Journal of Advanced Transportation

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In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of i -LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations.

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Section: Introductionmentioning

confidence: 99%

Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model

Cao

Wang

Long

2021

Journal of Advanced Transportation

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“…Some approaches in modeling this static background include using statistical component methods such as running average [3,4], and histogram analysis [5]. Otsu thresholding [6] is a threshold method in segmentation techniques, the application of the Otsu method makes it easier to do homogeneous division of parts based on similarity criteria to recognize objects.…”

Section: Introductionmentioning

confidence: 99%

Adaptive threshold for moving objects detection using gaussian mixture model

et al. 2020

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Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.

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“…The method in paper [9] can adaptively learn the learning rate parameters of the Gaussian model using a set of random samples which were recently observed. Due to the effectiveness of the GMM, many background modeling methods have been implemented based on it [10,11]. However, the algorithms based on GMM are computationally expensive because they need to calculate the mean and variance of all Gaussian distributions in each frame.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

2019

Sensors

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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.

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An adaptive background modeling based on modified running Gaussian average method

Cited by 8 publications

References 18 publications

Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model

Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model

Adaptive threshold for moving objects detection using gaussian mixture model

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

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