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| 摘要: |
| 背景减除法通过计算当前帧与背景模型的差来实现运动目标的检测, 因此背景建模是背景减除法的关键。混合高斯模型(Gaussian mixture model, GMM)可对存在渐变及重复性运动的场景进行建模,有效的提高了在光线强度变化，物体摇摆等复杂场景下建模的准确性。但它也有其固有缺点，本文针对利用传统EM算法进行GMM模型参数估计时，易陷入解空间的局部最优的缺陷，采用基于最大惩罚的EM参数估计，对传统的EM算法进行改进；另外，在检测不需要满足实时性时，提出了一种基于差分进化算法的GMM参数估计法；最后把改进的GMM参数估计方法应用于基于GMM模型的运动目标检测当中进行验证，并得到很好的检测效果。 |
| 关键词: 目标检测 GMM 参数估计 EM算法 差分进化 |
| DOI： |
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| Object Target detection method based on improved GMM parameter estimation |
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Li Shi-feng, Wen Zhi-qiang, Wu Yue-zhong
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| Abstract: |
| Background subtraction method through calculate the difference between the current frame and the background model to achieve the detection of moving targets, so the background modeling is a key to the method. Gaussian mixture model can model on the existence of a gradient and repetitive motion scene, and it effectively improve the accuracy of the modeling under the complex scenes of the light intensity changes and objects swing. But it also has its inherent drawbacks, In this paper，for?the?defect of easy to fall into the local optimum of the solution space when using traditional EM algorithm to estimate GMM parameters, we use the the maximum punishment of the likelihood function to resolve its deficiencies; in addition, we advance a differential evolution algorithm based GMM parameter estimation method when do not need to meet the real-time in the detection; Finally, we put the improved GMM parameter estimation method to applied to the moving target detection based on GMM model, and get a good result. |
| Key words: Object detection GMM Parameter estimation EM algorithm Differential evolution |
