复杂动态背景下的运动目标检测  

Moving object detection under complex dynamic background

作　　者：王思明[1] 韩乐乐 Wang Siming;Han Lele(School of Automation&Electrical Engineering,Lanzhou Jiao Tong University,Lanzhou,Gansu 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070

出　　处：《光电工程》2018年第10期170695-170701,共7页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(61263004)~~

摘　　要：为实现复杂动态背景下快速、准确地检测运动目标,提出一种改进二进制鲁棒不变尺度特征(BRISK)算法的运动目标检测方法。首先对图像进行分块,利用图像熵对图像块进行筛选;然后针对特征匹配过程中存在大量误匹配的问题,采用k近邻算法与欧氏距离进行特征匹配;最后通过改进的顺序抽样一致性算法进行特征点提纯,进一步完成背景运动补偿,从而利用形态学处理分割运动目标。采用多组视频图像进行验证,本文算法在原BRISK算法的基础上去除了32.7%的特征点,并且匹配效率提高了75%,处理速度比以往算法快,并且具有较强的抗噪性能。In order to realize fast and accurate detection of moving targets under complex dynamic background,a moving object detection method based on BRISK(binary robust invariant scalable keypoints)algorithm is proposed.Firstly,the image is divided into blocks,and the image blocks are filtered by using image entropy.Then,aiming at the problem of large number of mismatch in the process of feature matching,the k-nearest neighbor algorithm and Euclidean distance are used to perform feature matching.Finally,the improved sequential sampling consistency algorithm is used to refine the feature points and further completes the background motion compensation,and morphological processing is used to segment the moving target.Through the verification of multiple video images,the proposed algorithm removes 32.7%of the feature points based on the original BRISK algorithm and improves the matching efficiency by 75%.The proposed algorithm has faster processing speed than previous algorithms and strong anti-noise performance.

关 键 词：运动目标检测 BRISK算法 图像熵 欧氏距离 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]