改进的Mean Shift分割算法在安全区域识别中的研究

Research on improved mean shift segmentation algorithm in safe area recognition

为了提高在监控视频下进行安全区域识别的快速性及准确性,文中研究并提出了一种改进的Mean Shift(均值漂移)分割算法。首先简要介绍了小波变换与均值漂移分割算法的理论基础,再将二者融合后改进算法的计算转换到一个新的特征空间中进行,并将其应用于安全区域识别中,最后分别在纯净背景和复杂背景下进行安全区域识别对比实验。实验结果表明,文中的改进算法应用在安全区域识别中不但能更有效地消除噪声的干扰,同时具有更高的检测率和准确率,实现了更快的识别速度与更显著的识别效果。

In order to improve the speed and accuracy of safe area recognition in surveillance video,this paper studies and proposes an improved mean shift segmentation algorithm.Firstly,the theoretical basis of wavelet transform and mean shift segmentation algorithm is briefly introduced.Secondly,the calculation of the improved algorithm which fuse the above two algorithms is transformed into a new feature space,and it is applied to the recognition of safe area.Finally,the comparative experiments are carried out under pure background and complex background respectively.The experimental results show that the improved algorithm in this paper can not only eliminate the noise interference more effectively,but also has higher accuracy and detector rate.It realizes faster recognition speed and more significant recognition effect.