Background modeling method based on multi-feature fusion

Guo Zhicheng; Dang Jianwu; Wang Yangping; Jin Jing

doi:10.12086/oee.2018.180206

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Guo Zhicheng, Dang Jianwu, Wang Yangping, et al. Background modeling method based on multi-feature fusion[J]. Opto-Electronic Engineering, 2018, 45(12): 180206. doi: 10.12086/oee.2018.180206

Citation:

Guo Zhicheng, Dang Jianwu, Wang Yangping, et al. Background modeling method based on multi-feature fusion[J]. Opto-Electronic Engineering, 2018, 45(12): 180206. doi: 10.12086/oee.2018.180206

Background modeling method based on multi-feature fusion

1.
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
2.
Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou, Gansu 730070, China

Fund Project: Supported by Natural Science Foundation of China (61661026, 61162016), the Provincial Education Department of Gansu Province, China (2017D-08), the Natural Science Foundation of Gansu Province, China (1610RJZA039, 144WCGA162), and the Young Scholars Science Foundation of Lanzhou Jiaotong University (2016005)

More Information

Corresponding author: Dang Jianwu, E-mail:dangjw@mail.lzjtu.cn

Received Date 19 April 2018

Revised Date 31 May 2018

Published Date 01 December 2018

Abstract

Abstract

In order to build a robust background model and improve the accuracy of detection of foreground objects, the temporal correlation of pixels at the same position of the video image and the spatial correlation of neighboring pixels are considered comprehensively. This paper proposed a background modeling method based on multi-feature fusion. By using the domain correlation of pixels in a single frame image to quickly establish an initial background model whichis updated using pixel values, frequency, update time and sensitivity of the video image sequence, the ghost phenomenon is effectively improved and the holes and false prospects for moving targets are reduced. Through multiple sets of data tests, it shows that the algorithm improves the adaptability and robustness of dynamic background and complex background.
- multi-feature joint matching /
- motion detection /
- background modeling

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References

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Overview

Overview

Overview: Background modeling of moving targets is one of the research focal points and difficulties in machine vision and intelligent video processing. Its goal is to extract the change regions from the video sequence and effectively detect the moving targets for follow-up research such as object tracking, target classification, and application understanding such as behavior analysis and behavior understanding plays an important role. The commonly used detection methods include frame difference method, optical flow method, and background difference method. The background difference method has the advantages of small overhead, high speed, high accuracy, and accurate target extraction. It has become the most common method for detecting moving targets. The detection performance of the background difference method mainly depends on a robust background model. The background model establishment and update algorithm directly affects the detection effect of the final target. In order to build a robust background model and improve the accuracy of foreground detection, the temporal correlation of pixels in the same location of the video image and the spatial correlation of pixels in the neighborhood are considered comprehensively. This paper proposed a background modeling method based on multi-feature fusion. The rapid establishment of the initial background model with the first frame of the video sequence reduces the complexity of modeling sampling. The background model is updated using the video image sequence pixel values, frequency, update time, and adaptive sensitivity, wherein the adaptive sensitivity uses the feedback information of the pixel level background to adaptively acquire sensitivity for regions of different complexity to adapt to different complexity backgrounds. The high complexity background area has a high sensitivity, avoids the generation of erroneous front sights, and has a low complexity background area with less sensitivity and reduces misidentification of background points. The algorithm effectively improves the ghost phenomenon through multiple features, reducing the holes in the moving object in the foreground and the false foreground caused by pixel drift. In order to verify the effectiveness and practicability of the proposed algorithm, four background modeling algorithms, CodeBook, MOG, PBAS and ViBe, were selected for comparison experiments. Experiments selected Bootstrap, TimeOfDay, and WavingTrees in the Microsoft Wallflower paper dataset, highway, canoe, fountain02 in the CDNet2014 dataset, and were divided into three types of scene test algorithms: indoor, outdoor, and complex backgrounds. The test results show that this algorithm improves the adaptability and robustness of dynamic background and complex background.