Improved Adaptive Vibe and the Application for Segmentation of Complex Background

Author:

Chang Le1ORCID,Liu Zhenghua12,Ren Yan13ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

2. Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China

3. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

Abstract

To solve the problems that basic Vibe algorithm cannot effectively eliminate the influence of background noise, follower shadow, and ghost under complex background effectively, an adaptive threshold algorithm, AdaVibe, based on the framework of basic Vibe is proposed. Aiming at the shortage of the basic algorithm, this paper puts forward some improvement measures in threshold setting, shadow eliminating, and ghost suppression. Firstly, judgment threshold takes adjustment with the changes of background. Secondly, a fast eliminating ghost algorithm depending on adaptive threshold is introduced. Finally, follower shadow is detected and inhibited effectively through the gray properties and texture characteristics. Experiments show that the proposed AdaVibe algorithm works well in complex environment without affecting computing speed and has stronger robustness and better adaptability than the basic algorithm. Meanwhile, the ghost and follower shadow can be absorbed quickly as well. Therefore, the accuracy of target detection is effectively improved.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Interactive Image Segmentation of Graphic Design Based on Adaptive Local Threshold;2023 International Conference on Networking, Informatics and Computing (ICNETIC);2023-05

2. Moving target detection based on improved ghost suppression and adaptive visual background extraction;Journal of Central South University;2021-03

3. Device-Free Motion & Trajectory Detection via RFID;ACM Transactions on Embedded Computing Systems;2018-07-31

4. Background modelling using discriminative motion representation;IET Computer Vision;2017-07-12

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