塔机吊钩可视化系统的视频稳像算法

针对施工现场工况复杂,塔机吊钩视频稳像效果差的问题,本文提出了一种改进ORB特征匹配与固定滞后Kalman滤波相结合的吊钩视频稳像算法.在图像运动估计中,对经典ORB算法进行改进,采用了图像分块与自适应阈值的特征点提取,并引入图像四叉树算法提高图像特征点分布均匀性;在此基础上,采用背景补偿结合帧间差分法,快速识别局部运动目标并进行剔除,提高了全局运动参数估计的准确性;在运动滤波和补偿阶段,采用固定滞后Kalman滤波算法去除随机抖动分量,以获得视频去抖动的运动补偿参数,进而实现塔机吊钩可视化系统监控视频的稳像处理.实验结果表明:与经典ORB加Kalman滤波的稳像算法相比,本文所提出的稳像算法帧间变换保真度ITF提升了约9.12%,结构相似度平均值■提升了约2.75%,获得了更好的稳像效果,且帧处理速率FPS达到了29.65 f/s,满足塔机实时监控要求.

Video Stabilization Algorithm of Tower Crane Hook Visualization System

The tower crane hook video has poor video stabilization due to complex working conditions at the construction site. To solve the problem, this study proposes a hook video stabilization algorithm integrating the improved ORB feature matching and fixed-lag Kalman filter. In the image motion estimation, the classical ORB algorithm is improved by adopting image chunking and adaptive thresholding for feature point extraction and introducing the image quadratic tree algorithm to improve the uniformity of image feature point distribution; on this basis, the background compensation combined with inter-frame difference method is used to quickly identify local motion targets and reject them, which improves the accuracy of global motion parameter estimation; in the motion filter and compensation stage, a fixed-lag Kalman filter algorithm is used to remove the random jitter components, so as to obtain the motion compensation parameters for video stabilization, thus achieving video monitoring stabilization of the tower crane hook visualization system. The experimental results show that compared with that of the classical ORB and Kalman filter algorithm for image stabilization, the inter-frame transform fidelity (ITF) of the proposed algorithm is improved by about 9.12%, and the average value of structural similarity ($ \overline {{\text{SSIM}}} $) is improved by about 2.75%, resulting in a better image stabilization effect, and inter-frame processing speed (FPS) reaches 29.65 f/s, meeting the real-time monitoring requirements of the tower crane.