采用灰度加权核函数的动态背景运动目标检测算法

Detecting algorithm of moving target in dynamic background based on gray-weighted kernel function

针对动态图像序列中的运动目标检测存在的运算速度慢、虚警率高等问题,提出了一种基于灰度加权核函数的检测算法。算法首先利用图像中的平均梯度最大块实现了图像序列的快速配准,然后将图像分为32×32的子块,分别计算每一子块图像的灰度加权核函数(GWK),利用bhattacharyya系数作为配准后图像对应子块GWK函数的相似性度量,确认灰度加权核函数发生变化的子块,进而完成图像中的运动目标检测。实验结果表明,基于灰度核函数的运动目标检测算法运行速度快,可以有效抑制图像配准误差以及灰度起伏的影响,实时实现运动目标检测,具有较好的实时性和鲁棒性。

A new detecting algorithm based on gray-weighted kernel function was proposed to solve the proplem of low running rate and high false alarm within the moving target detection （MTD） in dynamic series of image. This algorithm firstly realized image sequence registration by using the biggest gradient block, then divided the image into 32×32 sub-images. It could calculate gray-weighted kernel function for every sub-image and detect changing of gray-weighted kernel function by using Bhattacharyya coefficient as similarity principle for every sub-image. The moving target could be detected in sub-image which gray-weighted kernel function has changed. The testing result shows that the algorithm with batter performance of real-time and robustness can detect the moving target in real-time and suppress the influence due to image registration error and gray fluctuation effectively.