基于多特征融合的运动阴影去除算法

对视频监控中的运动阴影问题进行了研究,提出一种颜色特征、归一化向量距离、亮度比值相融合的阴影去除方法。首先,通过混合高斯模型建立背景图像,利用背景差分法分离运动区域。然后,采用串行处理方法检测运动区域中的阴影像素。在RGB颜色空间下根据颜色一致性特征消除阴影之后,根据运动区域的归一化向量距离分布直方图进一步检测阴影像素。最后,针对阴影检测过程中存在的误检问题,建立像素的光照模型,计算阴影像素与背景像素的亮度比值,并根据置信区间排除误检的前景像素。实验结果表明,该方法能够克服单特征方法的局限性,在多个真实场景下能有效检测与去除阴影,适应性强,鲁棒性好,处理时间适中。

Moving Shadow Removal Algorithm Based on Multi-feature Fusion

Aiming at the problem of the moving cast shadow in the video surveillance,this paper proposed an shadow removal algorithm which combines color feature,normalized vector distance and intensity ratio.First,the background picture is built according to Gaussian mixture model,and motion region is acquired by background subtraction.Then,serial fusion method is adapted to detect and remove shadow pixels.Based on shadow detection according to the color consistent feature in RGB color space,the normalized vector distance distribution histogram is implemented to detect sha-dow pixels further.Finally,in view of the mistaken identification in the testing process,the illumination model of pixel is built and the intensity ratio of shadow pixel and background pixel is calculated to rule out the mistakenly identified foreground pixels according to the confidence interval.The results of experiment show that the proposed method can overcome the limitation of single feature method,and is able to detect and remove shadow under various circumstances efficiently.The adaptability and robustness of this algorithm are validated,and its processing time is moderate.