Moving object detection in video sequence images based on an improved visual background extraction algorithm

Zuo, Junhui; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

doi:10.1007/s11042-020-09530-0

Cited by 14 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The neighborhood standard deviation is used in the spatial dimension to measure the complexity of the background changes [26,27]. It measures the degree of difference between the current and average pixel values.…”

Section: Adaptive Thresholds In the Spatio-temporal Domainmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Liu,

Wei,

Yang

et al. 2023

Electronics

View full text Add to dashboard Cite

Foreground detection is the main way to identify regions of interest. The detection effectiveness determines the accuracy of subsequent behavior analysis. In order to enhance the detection effect and optimize the problems of low accuracy, this paper proposes an improved Vibe algorithm combining the frame difference method and adaptive thresholding. First, we adopt a shallow convolutional layer of VGG16 to extract the lower-level features of the image. Features images with high correlation are fused into a new image. Second, adaptive factors based on the spatio-temporal domain are introduced to divide the foreground and background. Finally, we construct an inter-frame average speed value to measure the moving speed of the foreground, which solves the mismatch problem between background change rate and model update rate. Experimental results show that our algorithm can effectively solve the drawback of the traditional method and prevent the background model from being contaminated. It suppresses the generation of ghosting, significantly improves detection accuracy, and reduces the false detection rate.

show abstract

Section: Adaptive Thresholds In the Spatio-temporal Domainmentioning

confidence: 99%

“…To quantitatively evaluate the quality criteria of the proposed algorithm, this paper evaluates the model's strengths and weaknesses in five aspects [26,27]: Accuracy, Precision, Recall, F-measure, and Balance error rate (PCW), and tests them on different datasets. These metrics are calculated as follows.…”

Section: Algorithm Performance Evaluationmentioning

confidence: 99%

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Liu,

Wei,

Yang

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…In the complex pipeline video collected by the collector, the value of each pixel follows a specific law. For complex pipeline video monitoring system, due to different monitoring scenes, the background of complex pipeline video usually presents single-mode characteristics [ 21 , 22 ].…”

Section: Cad Surface Generation Algorithm Of the Complex Pipeline Modelmentioning

confidence: 99%

Algorithm of CAD Surface Generation for Complex Pipe Model in Industry 4.0 Background

Cheng

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The current pipeline surface generation algorithm cannot get the angle information of the corner of complex pipeline surface, which leads to the poor accuracy of the algorithm, the slow speed of 3D point cloud intelligent mosaic, and the large number of effective points. Therefore, a CAD surface generation algorithm for complex pipeline model under the background of Industry 4.0 is designed, extracting and rendering the wireframe model and extracting background of the complex pipeline video. We obtain the angle information of the corner points of the complex pipeline surface, extract and match the feature of the dense point cloud, and construct the 3D point cloud data mosaic model. The pipe surface is generated by using double-nodal B-spline. The experimental results show that the precision and stability of the proposed method are high. In the early stage, the proposed method uses ISS feature extraction algorithm to extract feature of point cloud data, which improves the positioning accuracy effectively and enhances the 3D point cloud intelligent stitching speed.

show abstract

“…Vazquez and Bertalmío [17] proposed a digital boundary judgment algorithm based on the boundary judgment tree of important coefficients, which uses the boundary judgment of zero-tree coding and the masking of the human visual system to judge the boundary of the blue component (the least sensitive of the human eye) of the color image. Zuo et al [18] verified that the algorithm has good invisibility, but various image color gamut processing has a greater impact on the blue component, digital media may be lost in various image processing, and the robustness is poor [19].…”

Section: Related Workmentioning

confidence: 99%

Application of Image Color Gamut Boundary Judgment Algorithm in Digital Media

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Image chromatic aberration evaluation is one of the research hotspots and academic difficulties in the field of color science and imaging technology. Based on the algorithm theory of image color gamut boundary judgment, this paper constructs its application model in digital media. The model uses the threshold color difference image as a reference and applies it to the evaluation experiment of color difference above the threshold based on the quantitative estimation method. During the simulation process, the improved HAD dual-color image digital boundary judgment algorithm is programmed. For small color difference data at the threshold level, the calculated color difference of the boundary judgment algorithm framework is smaller than the visual perception color difference; for the large color difference data above the threshold, the boundary judgment algorithm framework is given. The research results of the threshold color difference experiment show that the perceptible color difference threshold and acceptable color difference threshold of image color gamut observers for image color difference are 1.85 AE and 3.63 AE, respectively, which are basically twice the relationship. The visually perceptible chromatic aberration threshold and acceptable chromatic aberration threshold for lightness parameters are higher than those of chroma and hue, which shows that the boundary judgment algorithm framework has certain advantages for the evaluation performance of color digital image threshold chromatic aberration and effectively improves the algorithm robustness.

show abstract

Moving object detection in video sequence images based on an improved visual background extraction algorithm

Cited by 14 publications

References 32 publications

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Algorithm of CAD Surface Generation for Complex Pipe Model in Industry 4.0 Background

Application of Image Color Gamut Boundary Judgment Algorithm in Digital Media

Contact Info

Product

Resources

About