带记忆存储的分级自组织背景差分算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201610003

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Abstract In the algorithm of self-organizing background subtraction, the real-time performance is poor and the background model is easy to offset under complicated environment due to non-periodic changes. Aiming at these problems, a hierarchical algorithm of self-organizing background subtraction with memory storage is proposed. Firstly, a border-shared background model is established to reduce the time and space complexity. And a cache strategy is introduced on the basis of the original matrix model to store the past and the current background data, respectively. Then, during the object detection, a decision mechanism for different granularity levels is designed to determine whether a pixel is an object or not. Experimental results show that the proposed algorithm can overcome the shortcomings of the original one and achieve higher detection accuracy and better real-time performance.

Key words： Self-Organizing Background Subtraction (SOBS) Memory Storage Hierarchical Judgement

Received: 08 March 2016

ZTFLH:

TP 391.41

Fund:Supported by National Natural Science Foundation of China (No.61502105,61473089)

About author:: (LIN Dewei, born in 1990, master student. His research inte-rests include image processing.)
(CHEN Zhaojiong(Corresponding author), born in 1964, master, professor. Her research interests include intelligent image processing and computational intelligence.)
(KE Xiao, born in 1983, Ph.D., lecturer. His research interests include pattern recognition, image processing, computer vision and machine learning.)
(YE Dongyi, born in 1964, Ph.D., professor. His research interests include computational intelligence and data mining.)

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	LIN Dewei
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Cite this article:

LIN Dewei,CHEN Zhaojiong,KE Xiao等. Hierarchical Algorithm of Self-Organizing Background Subtraction with Memory Storage[J]. , 2016, 29(10): 884-893.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201610003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I10/884

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