基于背景图像集与稀疏分析的运动目标检测

摘要

This paper proposed a moving target detection method based on the background image set and sparse representation. The method combined the Robust Principal Component Analysis (RPCA) and the image block analysis method based on sparse representation. The authors got a series of background images from a video sequence by RPCA, and combined these background images as the background image set, treating image block as basic unit, and moving target was extracted from input frame by image block analysis method based on sparse representation. The simulation results indicate that when the background illumination mutates, the proposed method can effectively eliminate the impact of environment noise and reduce the false detection rate of target detection.%针对环境光照变化时,现有背景建模方法不能有效检测运动目标的问题,给出了一种基于背景图像集与图块稀疏分析的运动目标检测方法.该方法融合了稳健主成分分析(RPCA)和基于稀疏表示的图块分析方法,通过RPCA从一组视频序列中得到系列背景图像,组合这些背景图像为背景集合.以图像块为基本单元,基于稀疏表示方法对图像块分析处理,提取运动目标.实验仿真表明该方法能够在环境光照突变时,有效消除噪声对目标检测的影响,降低目标检测的误检率,达到较为鲁棒的检测效果.