基于空间邻域相关性的运动目标检测方法

Moving Object Detection Based on Spatial Local Correlation

针对传统混合高斯模型运动目标检测准确度不高的问题,本文提出了一种改进的运动目标检测算法。该方法通过利用空间邻域的相关性信息,结合混合高斯模型来提高运动目标检测的准确性。首先,对图像中的每一像素建立高斯模型,并采用模型的匹配次数确定方差更新系数的算法,解决了传统方法中方差收敛缓慢的问题;然后重新定义了马尔科夫随机场的势能函数,并融入空间邻域的相关性信息,由此获得了用于运动目标检测的自适应阈值。采用IBM研究中心的测试视频序列对本文的方法进行了测试,实验结果表明,本文的方法对复杂的场景有较好的适应性,能够得到比较准确的检测结果。

The accuracy of moving object detection based on traditional Gaussian mixture model is not high. In order to solve this problem, an improved method for moving object detection was proposed, which could improve the accuracy of moving object detection by Gaussian mixture model with spatial local correlation. Firstly, the Gaussian mixture model was built for each pixel in image, and the algorithm of the updating the variance coefficients based on the number of model matching was used to solve the problem that the variance converged slowly in traditional method. Then, the energy function of the Markov random field was redefined and combined with the spatial local correlation, and an adaptive threshold was obtained for moving object detection. By using the public available test data set from IBM Research, the experiments were carried out. Test results illustrate that the proposed method can adapt to the dynamic scenes much better, and obtain more accurate results of moving object detection.