基于非负矩阵分解与相似性分析的运动目标检测

摘要

提出一种结合修正的非负矩阵分解与向量相似性分析进行运动目标检测的方法.该方法首先使用修正后的非负矩阵分解算法从连续图像序列中恢复出背景图像,然后分析待检测帧像素点与恢复出来的背景模型之间的相似性,根据相似性的高低区分背景与前景.为了减少计算量,降低动态背景对检测结果的干扰,该方法在进行相似性分析之前,通过核密度估计的方法对运动区域进行估计.实验结果表明,该方法能够较为精确地恢复出背景图像,并有效地检测出运动目标.%An algorithm of moving object detection fusing nonnegative matrix factorization(NMF)and vector similarity analysis is proposed.Firstly,the background is reconstructed from the continuous image sequence by using the modified NMF algorithm. Then,the similarity between the detected pixel and the recovered background model is analyzed,and the background and fore-ground are distinguished according to the similarity.In order to reduce the amount of computation and reduce the interference of dynamic background to the detection results,the method of kernel density estimation(KDE)is used to estimate the motion area before the similarity analysis is performed.The experimental results show that the proposed algorithm can recover the background image more accurately and detect the moving object effectively.