基于三维高斯混合码本模型的运动目标检测算法

为解决智能视觉监控中码本模型参数调节困难和高斯混合模型概率分布计算的复杂性,提出了一种基于三维高斯混合码本模型的运动目标检测算法.该算法基于RGB空间建立码本模型,然后基于码字中的R、G、B分量建立三维高斯模型,从而使整个码本具有三维高斯混合模型的特征.实验结果表明:该算法具有较高的实时性(该算法的平均帧率约23.0帧/s,而iGMM(improved Gaussian mixture model)算法约9.0帧/s,BM(Bayesmodel)算法约6.2帧/s,CBM(codebook model)算法约10.7帧/s),且具有良好的检测质量.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model

In order to solve the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance,a moving objects detection algorithm based on the three-dimensional Gaussian mixture codebook model was proposed.In this algorithm a codebook model based on RGB space is built,and then a three-dimensional Gaussian model based on R,G and B components in codewords is established.In this way,the characteristic of the three-dimensional Gaussian mixture model for the codebook model can be obtained.The experimental results show that the average frame rate of the proposed algorithm is about 23.0 frames per second,while it is about 9.0 frames per second for the iGMM(improved Gaussian mixture model) algorithm,about 6.2 frames per second for the BM(Bayes model) algorithm,and about 10.7 frames per second for CBM(codebook model) algorithm in the comparative experiments.Furthermore,the proposed algorithm can obtain a good detection quantity.