下雪天气条件下的运动目标检测

Moving Object Detection in Snowy Weather Condition

针对下雪天气条件下高精度的运动目标检测,本文在GMM算法基础上进行改进,首先采用多分辨率、高低阈值的思想对其进行优化,克服下雪天动态背景噪声的影响;然后运用计算颜色模型,抑制运动目标产生的弱阴影和光照变化;最后在各目标最小约束矩形内进行空洞修补,填充由于阴影过度抑制和被雪覆盖目标表面丢失的运动掩模。实验结果表明:改进算法7项指标都优于GMM算法,与当前较优秀的FTSG算法相比,7项指标中有4项超越,2项接近。

In order to realize the high precision moving object detection in snowy weather condition, this paper improves the GMM algorithm. Firstly, the multi-resolution, high and low threshold concepts are used to optimize the detection results, which can overcome the influence of the dynamic background noise. Then, the color model is used to suppress the weak shadows and illumination changes by moving objects. Finally, the hole is filled in the rectangle with the minimum constraint of each object, and the motion mask is filled due to the excessive suppression of the shadow and the loss of the surface covered by snow. Experimental results show that the improved algorithm is better than the GMM algorithm for all of the seven indicators. Compared with the current outstanding algorithm FTSG, there are four of the seven transcend, the two close.