Feature-based Classification for Image Segmentation in Automotive Radar Based on Statistical Distribution Analysis
Feature-based Classification for Image Segmentation in Automotive Radar Based on Statistical Distribution Analysis
复制标题
基于统计分布分析的汽车雷达图像分割特征分类
DOI：
10.1109/radarconf2043947.2020.9266596
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Gashinova
中科院分区：
文献类型：
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作者：
Yang Xiao;L. Daniel;M. Gashinova
汽车雷达图像中表面和障碍物区域的分割和潜在分类是自动驾驶中有效路径规划的关键因素。与传统的雷达处理不同，杂波被认为是不必要的返回，应该被有效地去除，自动驾驶需要全场景评估，杂波携带自主平台态势感知的必要信息，需要进行全面评估以找到可通过的区域。本文研究了沥青、草地、阴影和目标物区域等几种与道路相关场景的雷达强度数据的统计分布特征。提出了基于分布特征提取和多元高斯分布（MGD）模型的分类算法。在测试下，使用多传感器服记录的数据集来评估该分类算法的混淆矩阵和F1得分。
Segmentation and potential classification of surface and obstacle regions in automotive radar imagery is the key enabler of effective path planning in autonomous driving. As opposed to traditional radar processing where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene assessment, where clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, the statistical distribution features of the radar intensity data of several road-related scenes including asphalt, grass, shadow and target object areas are investigated. The algorithm of classification is developed based on distribution feature extraction and a multivariate Gaussian distribution (MGD) model. Under test dataset recorded by multi-sensor suit was used to evaluate the confusion matrix and F1 score of this classification algorithm.
DOI：
10.23919/irs.2019.8768106
发表时间：
2019-06
期刊：
2019 20th International Radar Symposium (IRS)
影响因子：
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作者：
L. Daniel;D. Phippen;E. Hoare;M. Cherniakov;M. Gashinova
通讯作者：
L. Daniel;D. Phippen;E. Hoare;M. Cherniakov;M. Gashinova