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一种基于GLCM的运动目标检测新方法 被引量:1

A Novel Detection Method of Moving Object Based on GLCM
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摘要 基于GMM(Gaussian Mixture Model,混合高斯模型)的物体识别基础上,利用GLCM(Gray-level co-occurrence matrix,灰度共生矩阵)及基于GLCM提取的纹理特征来解决当前运动目标检测所存在的问题,如动态场景的变化,光照突变及天气变化等。GLCM在局部区域的往复运动具有相对不变性,因此利用这个特点对基于GMM检测的前景进行再判断以解决动态场景的问题,将检测窗口中当前帧和前两帧的GLCM特征值进行比较,如果其GLCM特征值的差值小于给定的阈值,那么可以判断当前区域为背景,反之则为前景。图像的纹理特征具有抗光照突变性,经过分析其中的4个特征值并阈值化最终得到更加纯净的前景和更加准确的检测结果。通过CPU/GPU(Central Process Unit/Graphic Processing Unit)协同并行计算大大加速了运动目标检测过程。实验证明这种新的检测算法在检测精度和处理速度上比其他算法有明显改善。 For current moving object detection algorithms ,therexist three main issues ,inclu‐ding dynamic scene ,sudden illumination changes ,and bad weathers .These problems reduce the performance of video segmentation .The Gaussian Mixture Model algorithm is widely used cur‐rently .T he improved moving object detection algorithm based on GLCM for video surveillance is presented in this paper .The proposed algorithm computes the Gray‐level co‐occurrence matrix (GLCM ) to eliminate the effect of dynamic background by threshold firstly .Secondly ,it extracts the texture which has the property of resisting sudden change in light according to the GLCM .Fi‐nally ,cleaner foreground and more accurate results are obtained .What’s more ,the algorithm runs efficiently through CPU/GPU collaborative computing in parallel .
出处 《太原理工大学学报》 CAS 北大核心 2015年第6期719-726,共8页 Journal of Taiyuan University of Technology
基金 国家自然科学基金项目:面向实时并发数据流的能耗优化的GPU集群可靠处理机制研究(61572325) 高等学校博士学科点专项科研博导基金(20113120110008) 上海重点科技攻关项目(14511107902) 上海市工程中心建设项目(GCZX14014) 上海智能家居大规模物联共性技术工程中心项目(GCZX14014) 上海市一流学科建设项目(XTKX2012) 沪江基金研究基地专项(C14001)资助
关键词 灰度共生矩阵 运动目标检测 纹理特征 混合高斯模型 协同计算 GLCM Moving Object Extraction Texture Gaussian Mixture Model Collabo-rative Computing
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参考文献9

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