基于双目视觉的四边形闭环跟踪算法

alibaba

基于双目视觉的四边形闭环跟踪算法

樊俊杰 1;2，梁华为 2，祝 辉 2，余 彪 2

（1. 中国科学技术大学自动化系，安徽 合肥 230027；2. 中国科学院合肥物质科学研究院应用技术研究所，安徽 合肥 230028）

摘 要：针对动态背景下视觉跟踪存在的错误率大、鲁棒性不强以及多目相机信息融合难等问题，提出一种基于双目匹配的视觉跟踪算法．结合双目相机的物理结构特点，该算法以四边形闭环方法对特征点进行匹配，实现特征点的双目匹配和立体跟踪，并优化匹配的搜索结构．算法首先采用高斯－拉普拉斯模板对图像进行滤波，利用 Harris 角点检测算法提取特征点并依次构造描述子序列，然后以绝对误差和作为匹配代价的衡量准则，建立四边搜索准则进行邻近搜索，同时引入 RANSAC（随机采样一致性）算法进行可靠性筛选，最终通过 4 点构成的四边形闭环检测实现了特征点跟踪精度的改进．通过对不同分辨率、不同路况图像集进行测试实验，所提出的四边形跟踪算法特征跟踪正确率可达 99.80%，鲁棒性和精度均优于光流法．
关键词：视觉跟踪；信息融合；双目匹配；四边形闭环
中图分类号：TP391.41 文献标识码：A 文章编号：1002-0446(2015)-06-0674-09

Closed Quadrilateral Feature Tracking Algorithm Based on Binocular Vision

FAN Junjie1;2，LIANG Hauwei2，ZHU Hui2，YU Biao2

(1. Department of Automation, University of Science and Technology of China, Hefei 230027, China;2. Institute of Applied Technology, Hefei Institutes of Physical Science,Chinese Academy of Sciences, Hefei 230028, China)

Abstract: A visual tracking algorithm based on binocular matching is proposed to solve the problems such as high error rate, low robustness and poor information fusion existing in current visual tracking methods under dynamic background.Based on the physical structure of binocular camera, the proposed algorithm adopts a closed quadrilateral method to match feature points so as to implement the binocular matching and 3D tracking and optimize the searching structure. The binocular images are filtered by Gauss-aplace template, and the feature points extracted by the Harris corner detection algorithm are encapsulated orderly into feature descriptor. Then, the sum of absolute difference is used as the matching criteria, a 4-side searching criteria is set up for neighbor searching, and RANSAC (random sample consensus) algorithm is introduced for reliability screening. Finally, the accuracy of feature tracking is improved through the 4 points formed quadrilateral closed-loop detection. To evaluate the performance of the proposed method, images with different resolutions under different road conditions are collected, and test experiments are conducted. The experimental results show that the average tracking precision of the proposed method reaches 99.80%. The robustness and tracking accuracy of the proposed method is superior to the optical flow method.
Keywords: visual tracking; information fusion; binocular matching; closed quadrilateral


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