计算机科学 ›› 2014, Vol. 41 ›› Issue (3): 293-296.

• 图形图像与模式识别 • 上一篇    下一篇

基于局部强度比率特征的前景检测

向金海,廖红虹,樊恒,代江华,孙伟平,余胜生   

  1. 华中科技大学计算机学院 武汉430074;华中科技大学计算机学院 武汉430074;华中农业大学理学院 武汉430070;华中科技大学计算机学院 武汉430074;华中科技大学计算机学院 武汉430074;华中科技大学计算机学院 武汉430074
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受中央高校基本科研业务费专项资金资助

Robust Foreground Detection Using Local Intensity Ratio

XIANG Jin-hai,LIAO Hong-hong,FAN Heng,DAI Jiang-hua,SUN Wei-ping and YU Sheng-sheng   

  • Online:2018-11-14 Published:2018-11-14

摘要: 对运动的真实前景目标进行实时提取是监控视频中的一个基本步骤。在前景提取过程中,阴影消除一直是一个较难解决的问题。为解决此问题,根据光照模型提出了局部强度比率模型,并证明其具有光照不变性特征。同时证明,如果视频图像噪声高斯分布,则局部强度比率也满足高斯分布。在通过高斯混合模型获取前景的过程中,用局部强度比率代替像素值进行处理,得到消除阴影后的前景。实验表明,本方法在不同的场景下可以有效地消除阴影,得到无阴影的前景,同其他方法比较,显示出较好的性能。

关键词: 局部强度比率,阴影消除,高斯混合模型,光照变化 中图法分类号TP391.41文献标识码A

Abstract: Real time segmentation of foreground objects in video sequences is a fundamental step for surveillance.This paper proposed a local intensity ratio(LIR) to remove shadow.The local intensity ratio has the illumination invariance feature which is based on the analysis of illumination change model.The distribution of the LIR was discussed.We used the local intensity ratio instead of pixel intensity for Gaussian Mixture Model(GMM),and then got the foreground without shadow.Based on experimental results,the LIR feature shows excellent performance under various illumination change conditions while operating in real-time.

Key words: Local intensity ratio(LIR),Shadow remove,Gaussian mixture model (GMM),Illumination change

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