Background Subtraction Based on Codebook Model and Texture Feature

Lin, Geng-Cheng; Yang, Shuenn-Chen; Wang, Chuin-Mu; Lin, Chih-Wei

doi:10.1109/is3c.2016.130

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Moving objects can be detected by this method, but errors occur when there is sudden light or changes of light. In , a BS method based on the combination of the codebook model and texture is proposed. This method can improve the accuracy of detection and has good adaptability, but the detection effect of this method is not very satisfying in a dynamic background.…”

Section: Relative Researchesmentioning

confidence: 99%

Improved background subtraction method for detecting moving objects based on GMM

Wang

et al. 2018

IEEJ Transactions Elec Engng

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The background subtraction (BS) method is one of the most commonly used components in the detection of moving objects in computer vision systems, in which moving objects in image sequences are detected by comparing the background model with the current frame. Traditional moving object detection algorithms have some problems, such as changes in dynamic background, background variation due to changes of illumination, and being more sensitive to noise and shadows. In this study, an improved BS method based on the Gaussian mixture model (GMM) is proposed for the challenge in object detection. Not only is the influence of changes of illumination, and noise and shadows reduced, but the dynamic changes of natural scenes can also be tackled by using this method. The contribution lies in the following aspects: (i) a Gaussian background modeling method with less running time is proposed in the background modeling stage. The background is reconstructed based on GMM of the mean images of image blocks, aiming to simplify the calculations so as to improve the speed of the corresponding operations. (ii) In the foreground detection stage, a wavelet‐based, denoising method with the semisoft threshold function is applied to denoise the object images of the foreground. (iii) In the background maintenance stage, an adaptive background maintenance algorithm is proposed to dynamically update the background. Experimental results show that the computational complexity is reduced, while the adaptability and performance are improved by using this method. It outperforms the traditional methods in terms of both efficiency and robustness. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Relative Researchesmentioning

confidence: 99%

Improved background subtraction method for detecting moving objects based on GMM

Wang

et al. 2018

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…However, its computational complexity is very high. The background subtraction method is another sequence target detection technology, such as the spatiotemporal background model method [ 46 ], the coding model background subtraction method [ 47 ], etc. These algorithms are simple in principle and fast in detection; however, when the actual background changes, the accuracy of the detection results will also change.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Based on Multiscale Kurtosis Map Fusion and Optical Flow Method

Xin

Cao

Xiao

et al. 2023

Sensors

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The uncertainty of target sizes and the complexity of backgrounds are the main reasons for the poor detection performance of small infrared targets. Focusing on this issue, this paper presents a robust and accurate algorithm that combines multiscale kurtosis map fusion and the optical flow method for the detection of small infrared targets in complex natural scenes. The paper has made three main contributions: First, it proposes a structure for infrared small target detection technology based on multiscale kurtosis maps and optical flow fields, which can well represent the shape, size and motion information of the target and is advantageous to the enhancement of the target and the suppression of the background. Second, a strategy of multi-scale kurtosis map fusion is presented to match the shape and the size of the small target, which can effectively enhance small targets with different sizes as well as suppress the highlighted noise points and the residual background edges. During the fusion process, a novel weighting mechanism is proposed to fuse different scale kurtosis maps, by means of which the scale that matches the true target is effectively enhanced. Third, an improved optical flow method is utilized to further suppress the nontarget residual clutter that cannot be completely removed by multiscale kurtosis map fusion. By means of the scale confidence parameter obtained during the multiscale kurtosis map fusion step, the optical flow method can select the optimal neighborhood that matches best to the target size and shape, which can effectively improve the integrity of the detection target and the ability to suppress residual clutter. As a result, the proposed method achieves a superior performance. Experimental results on eleven typical complex infrared natural scenes show that, compared with seven state-of-the-art methods, the presented method outperforms in terms of subjective visual effect, as well as some main objective evaluation indicators such as BSF, SCRG and ROC, etc.

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On the role and the importance of features for background modeling and foreground detection

Bouwmans

Silva

Marghes

et al. 2018

Computer Science Review

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Background Subtraction Based on Codebook Model and Texture Feature

Cited by 4 publications

References 13 publications

Improved background subtraction method for detecting moving objects based on GMM

Improved background subtraction method for detecting moving objects based on GMM

Infrared Small Target Detection Based on Multiscale Kurtosis Map Fusion and Optical Flow Method

On the role and the importance of features for background modeling and foreground detection

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