摘要
为进一步提高事件传感器光流估计的精确度,解决平面拟合算法的拟合模型误差问题,提出一种事件传感器平面拟合光流估计改进算法。该算法采用Prim贪婪算法思想,从事件流中提取有效事件,获取最优局部邻近事件集,为后续光流估计奠定基础。同时,采用特征值算法代替传统最小二乘法,结合贪婪算法下事件集内数据的优劣排序,优化平面拟合模型的建立,提高光流估计算法的精确度。实验结果表明,相比于现有的基于平面拟合的事件传感器光流估计算法,该算法在平均端点误差和平均角度误差两个指标上分别约提升20%和11%,有效提升了事件传感器光流估计算法的精确度。
An improved algorithm for estimating the plane-fitting optical flow is proposed to further improve the accuracy of event sensor optical flow estimation and solve the fitting model error of the plane-fitting algorithm.The algorithm adopts the principle of the Prim greedy algorithm to extract effective events from the event flow and obtain the optimal local adjacent event set,laying the foundation for subsequent optical flow estimation.In addition,the eigenvalue algorithm is used to replace the traditional least square method,and the data ranking of the event set under the greedy algorithm is combined to optimize the plane-fitting model and improve the accuracy of the optical flow estimation algorithm.The experimental results show that compared with the existing estimation algorithm for event sensor optical flow based on plane-fitting,the algorithm improves the average endpoint error and average angle error by 20%and 11%,respectively.This study demonstrates effective improvement of the accuracy of the event sensor optical flow estimation algorithm.
作者
郭爽
徐江涛
高志远
张磊
Guo Shuang;Xu Jiangtao;Gao Zhiyuang;Zhang Lei(School of Microelectronics,Tianjin University,Tianjin 300072,China;Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology,Tianjin 300072,China;School of Software and Communication,Tianjin Sino-German University of Applied Sciences,Tianjin 300350,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2023年第14期255-263,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(62134004)
天津市科技计划项目(20YDTPJC00890)。
关键词
机器视觉
光流估计
平面拟合
PRIM算法
特征值法
随机抽样一致算法
machine vision
optical flow estimation
plane-fitting
Prim algorithm
eigenvalue algorithm
random sample consensus algorithm