Traditional template matching-based motion estimation is a popular but time-consuming method for vibration vision measurement. In this study, the particle swarm optimization (PSO) algorithm is improved to solve this time-consumption problem. The convergence speed of the algorithm is increased using the adjacent frames search method in the particle swarm initialization process. A flag array is created to avoid repeated calculation in the termination strategy. The subpixel positioning accuracy is ensured by applying the surface fitting method. The robustness of the algorithm is ensured by applying the zero-mean normalized cross correlation. Simulation results demonstrate that the average extraction error of the improved PSO algorithm is less than 1%. Compared with the commonly used three-step search algorithm, diamond search algorithm, and local search algorithm, the improved PSO algorithm consumes the least number of search points. Moreover, tests on real-world image sequences show good estimation accuracy at very low computational cost. The improved PSO algorithm proposed in this study is fast, accurate, and robust, and is suitable for plane motion estimation in vision measurement.

中文翻译：

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振动视觉测量的改进粒子群优化方法

传统的基于模板匹配的运动估计是一种流行但耗时的振动视觉测量方法。在这项研究中，改进了粒子群优化（PSO）算法来解决这个耗时问题。在粒子群初始化过程中使用相邻帧搜索方法提高了算法的收敛速度。创建一个标志数组以避免终止策略中的重复计算。采用曲面拟合法保证亚像素定位精度。通过应用零均值归一化互相关来确保算法的鲁棒性。仿真结果表明改进的粒子群优化算法的平均提取误差小于1%。与常用的三步搜索算法、菱形搜索算法相比，与局部搜索算法相比，改进的 PSO 算法消耗的搜索点数最少。此外，对真实世界图像序列的测试以非常低的计算成本显示出良好的估计精度。本研究提出的改进PSO算法快速、准确、鲁棒，适用于视觉测量中的平面运动估计。