Due to the problems of slow speed and error drift in the positioning of large-scale long-sequence Unmanned Aerial Vehicle (UAV) images, a positioning algorithm of UAV images based on scene graph division was proposed according to the characteristics of UAV images. Firstly, the Global Positioning System (GPS) ancillary information was used to narrow the spatial search scope for feature matching, so as to accelerate the extraction of corresponding points. After that, visual consistency and spatial consistency were combined to construct the scene graphs, and Normalized Cut (Ncut) was used to divide them. Then, incremental reconstruction was performed to each group of scene graphs. Finally, all scene graphs were fused to establish a 3S scene model by Bundle Adjustment (BA). In addition, the GPS spatial constraint information was added to the cost function in the BA stage. In the experiments on four UAV image datasets, compared with COLMAP and other Structure From Motion (SFM) algorithms, the proposed algorithm has the positioning speed increased by 50%, the reprojection error decreased by 41%, and the positioning error was controlled within 0.5 m. Through the experimental comparison of algorithms with or without GPS assistance, it can be seen that BA with relative and absolute GPS constraints solves the problem of error drift, avoids the ambiguous results and greatly reduces positioning error.

为了克服传统密度估计方法受限于算法配置工作量高、高等级密度样本数量有限等因素无法大规模应用的缺点,提出一种基于监控视频的全景密度估计方法。首先,通过自动构建场景的权重图消除成像过程中射影畸变造成的影响,该过程针对不同的场景自动鲁棒地学习出对应的权值图,从而有效降低算法配置工作量;其次,利用仿真模拟方法通过低密度等级样本构建大量高密度等级样本;最后,提取训练样本的面积、周长等特征用于训练支持向量回归机(SVR)来预测每个场景的密度等级。在测试过程中,还通过二维图像与全景地理信息系统(GIS)地图的映射,实时展示全景密度分布情况。在北京北站广场地区的深度应用结果表明,所提全景密度估计方法可以准确、快速、有效地估计复杂场景中人群密度动态变化。