结合光流法与最近邻算法的运动目标检测

Moving Object Detection Method Combined Optical Flow and K-Nearest Neighbor Algorithm

运动目标检测具有广泛的理论和现实意义,光流法是检测运动目标的重要方法之一。但是用于运动目标检测的光流算法却有着计算量大、处理复杂的问题。一种聚类分析算法和改进的LK光流法相结合检测方案可以很好地解决此类问题。对基于改进的LK光流法的运动目标检测算法进行了分析和仿真,再加以聚类分析使得检测出的运动目标更加准确。首先对图像序列进行采样与预处理,并利用LK光流法计算得出相邻帧图像的光流场,然后再利用最近邻聚类算法对得到的光流场进行处理,进而检测出图像中的运动目标,最后使用Matlab软件进行算法程序验证。通过实验可知,基于金字塔LK光流法与最近邻算法的运动目标检测方案可以更加有效地检测出运动物体。

Moving object detection has great significance in theory and practice,and optical flow is one of the important moving object detection methods. A method to detect moving objects combined K-Nearest neighbor algorithm and pyramid LK optical flow is proposed to solve such problems,as the optical flow algorithm for moving target detection has a large computational complexity and a complex problem. Analysis and simulation of moving target detection based on the improved LucasKanade optical flow algorithm are given,and then the clustering analysis is made to make the moving object more accurate.Firstly,the image sequences of moving target are sampled and processed. Then Lucas-Kanade optical flow method is used to calculated the adjacent frames of light flow to get the optical flow field. Lastly,the K-Nearest neighbor algorithm is used to detect the moving object. The experiments prove that the object detection algorithm combined Lucas-Kanade algorithm and KNearest neighbor algorithm is an efficient moving target detection method.