结合单高斯与光流法的无人机运动目标检测

针对无人机场景下运动目标检测对实时性要求高，运动背景、环境光照易变化等问题，提出一种结合单高斯与光流法的运动目标检测算法.首先，对运动相机捕捉的图像采用改进的单高斯模型进行背景建模，并融合前一帧图像的多个高斯模型来进行运动补偿，然后，将得到的前景图像作为掩模来提取特征点和进行光流跟踪，并对稀疏特征点的运动轨迹进行层次聚类.实验结果表明，该算法能有效地处理运动相机造成的前景对背景模型的干扰，背景建模速度快，对光照变化不敏感，检测出的目标接近真实目标.

Detection of Moving Objects in UAV Video Based on Single Gaussian Model and Optical Flow Analysis

To meet the real-time demand of moving object detection in Unmanned Air Vehicle (UAV), and to cope with the problems of moving background and variable illumination, a novel moving object detection technique based on Single Gaussian Model (SGM) and optical flow is presented. First, an improved SGM is applied to model the background of the image captured by moving camera, and then the corresponding models of previous frame are fused to compensate the motion of camera. Second, the obtained foreground is used as a mask to extract feature points to calculate optical flow, and then these sparse points are clustered to detect the objects. Experimental results demonstrated the effectiveness of the proposed approach in preventing the background model of SGM from being contaminated by the foreground, as well as dealing with illumination changes. It can also update background model quickly and obtain moving objects precisely.