Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (7): 233-237.doi: 10.11896/jsjkx.200600131

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Moving Object Detection Based on Region Extraction and Improved LBP Features

XIN Yuan-xue, SHI Peng-fei, XUE Rui-yang   

  1. College of Internet of Things Engineering,Hohai University,Changzhou,Jiangsu 213022,China
  • Received:2020-06-22 Revised:2020-08-30 Online:2021-07-15 Published:2021-07-02
  • About author:XIN Yuan-xue,born in 1987,Ph.D,lecturer.Her main research interests include communication system, information acquisition and processing.
  • Supported by:
    National Natural Science Foundation of China(61801168, 61801169) and Jiangsu Province Natural Science Foundation(BK20170305).

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Abstract: Detection accuracy of moving object is dramatically affected by the dynamic natural background,for instance,the shaking leaves and varying illumination.Therefore,it is essential to distinguish between the dynamic background and the foreground moving object.The existing foreground extraction algorithm extracts the foreground point by point,which leads to a waste of computing resources.This paper proposed a novel moving object detection algorithm based on region extraction and improved Local Binary Patterns (LBP).First,the image is divided into several image blocks of same size,and the Kernel Density Estimation (KDE) model is established according to the statistical characteristics of these image blocks.The foreground region is estimated by the KDE model.Then,the improved LBP texture feature histogram of all pixels in the foreground block is obtained.By ma-tching the histogram,all the foreground pixels are extracted,and the background is updated with a probabilistic model.The experimental results show that the proposed method can quickly extract the foreground region of moving target and eliminate most of the interference caused by dynamic background.Compared with the traditional algorithm,the proposed method is more suitable for moving object detection in natural scenes.

Key words: Dynamic background, Kernel density estimation, Local binary patterns texture features, Moving object detection, Region extraction

CLC Number: 

  • TP391.41
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