一种对光照变化鲁棒的移动目标前景提取方法

运动目标前景提取是对其进一步分析如特征提取、行为分析等的基础。RPCA(鲁棒主成分分析)分解可以得到较为完整的目标前景,但该方法对光照变化敏感,容易导致误检。利用Lab颜色空间中a,b通道对光照变化不敏感的特点,可以提高基于RPCA分解的前景提取方法对光照变化的鲁棒性,首先对图像L,a,b通道分别进行RPCA分解得到稀疏前景,然后利用大津阈值分割各通道二值化前景并采用种子点填充技术融合不同前景,最后利用形态学滤波优化融合结果提取准确的运动目标前景。实验结果表明,该方法可以在复杂背景下准确提取运动目标前景,且能有效克服光照变化的影响。

Robust Moving Object Foreground Extraction Approach to Illumination Change

Foreground extraction of moving object is the foundation for further analysis.An almost complete object foreground can be obtained by RPCA (Robust Principal Component Analysis) decomposition.However,this approach is sensitive to illumination change.The robustness of RPCA-based foreground extraction approach can be increased by the fact that a,b channels of Lab color space are not sensitive to illumination change.Initially,the sparse foregrounds of L,a,b channels are calculated based on RPCA decomposition respectively.Then Ostu thresholds are employed for binary foreground segmentation of each channel and seed filling technology is utilized for fusing different foregrounds.Finally,the accurate moving object foregrounds are extracted after improving the fusion results with morphology filtering.The experimental results indicate that the proposed method can accurately extract object foreground under complex environments while handling illumination change effectively.