【摘要】 传统的高斯混合建模算法对阴影的抑制效果差,且存在噪声干扰和对光照突变比较敏感的问题。采用了一种改进的高斯混合建模方法进行运动目标轮廓提取。该方法利用Canny边缘图像对噪声和光照适应性强的特点,将传统高斯混合模型与Canny边缘检测相结合来提取目标轮廓。但是,该方法复杂度高且计算量大,不满足视频分析实时性的需求,因此,运用GPU强大计算能力和并行处理的优势,基于CUDA平台设计并实现了该运动目标轮廓提取算法。实验结果表明,该算法增强了对噪声和光照的适应性,且有效抑制了图像中的阴影,在保证效果的前提下能够更快速地提取视频序列中的运动目标轮廓。更多还原

【Abstract】 Traditional Gaussian mixture modeling algorithm has the poor shadow suppression,and the problem of noise and being sensitive to illumination change also exist.In this paper,an improved Gaussian mixture modeling algorithm is used to extract the contour of moving objects.Gaussian mixture model is combined with Canny edge detection to extract the contour of objects with the help of the strong adaptability of Canny edge image to noise and illumination.However,this method is of high complexity and large amount of calculation,which can not meet the requirements of real-time video analysis.So this paper designs and implements the algorithm of contour extraction of moving objects based on CUDA platform by using GPU’s advantages of powerful computing power and parallel processing.Experimental results indicate that the algorithm improved the adaptability of noise and illumination,and effectively suppress the shadow in images.It can extract the contour of the moving objects in video sequences faster than in general environment under the precondition of the effect.更多还原