Parking-vehicles recognition using spatial temporal data (a study of mobile robot surveillance system using spatial temporal GIS part 2)

IEEE International Safety, Security and Rescue Rototics, Workshop, 2005. Pub Date : 2005-06-06 DOI:10.1109/SSRR.2005.1501264

K. Ishikawa, J. Meguro, Y. Amano, T. Hashizume, J. Takiguchi, R. Kurosaki, M. Hatayama

{"title":"Parking-vehicles recognition using spatial temporal data (a study of mobile robot surveillance system using spatial temporal GIS part 2)","authors":"K. Ishikawa, J. Meguro, Y. Amano, T. Hashizume, J. Takiguchi, R. Kurosaki, M. Hatayama","doi":"10.1109/SSRR.2005.1501264","DOIUrl":null,"url":null,"abstract":"The unique omni-directional motion stereo method featuring robust epipolar estimation and hybrid use of the feature/area based matching, and the change-region recognition technique which uses dense color-textured depth map and 3D-GIS data for segmentation, are presented. The dense stereo imaging data, which is acquired by the coupled use of an ODV (omni-directional vision) and a GPS/INS (Inertial Navigation Systems) by the motion stereo method, is classified in \"change region\" or \"registered region\" by the D-GIS's geometric model of the building. Hence, the changeable region like a parking-vehicle on the road is modeled as a hexahedron through surface recognition process, the position of vertexes and the image texture of three surfaces are measured, and are additionally registered in the spatial temporal GIS (Geographic Information System) as new object data. The proposed method can be applicable to a mobile robot surveillance system which is used in a site immediately after a disaster.","PeriodicalId":173715,"journal":{"name":"IEEE International Safety, Security and Rescue Rototics, Workshop, 2005.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Safety, Security and Rescue Rototics, Workshop, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSRR.2005.1501264","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 9

Abstract

The unique omni-directional motion stereo method featuring robust epipolar estimation and hybrid use of the feature/area based matching, and the change-region recognition technique which uses dense color-textured depth map and 3D-GIS data for segmentation, are presented. The dense stereo imaging data, which is acquired by the coupled use of an ODV (omni-directional vision) and a GPS/INS (Inertial Navigation Systems) by the motion stereo method, is classified in "change region" or "registered region" by the D-GIS's geometric model of the building. Hence, the changeable region like a parking-vehicle on the road is modeled as a hexahedron through surface recognition process, the position of vertexes and the image texture of three surfaces are measured, and are additionally registered in the spatial temporal GIS (Geographic Information System) as new object data. The proposed method can be applicable to a mobile robot surveillance system which is used in a site immediately after a disaster.

查看原文本刊更多论文

基于时空数据的停车车辆识别(基于时空GIS的移动机器人监控系统研究第二部分)

提出了具有鲁棒极极估计和混合使用特征/区域匹配的独特的全方位运动立体方法，以及使用密集彩色纹理深度图和3D-GIS数据进行分割的变化区域识别技术。利用全向视觉系统(ODV)和惯性导航系统(GPS/INS)的运动立体方法，将密集的立体成像数据通过D-GIS的建筑物几何模型划分为“变化区”或“注册区”。因此，将道路上停放的车辆等可变区域通过表面识别建模为六面体，测量三个表面的顶点位置和图像纹理，并作为新的目标数据注册到时空地理信息系统中。该方法适用于灾害发生后立即在现场使用的移动机器人监控系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE International Safety, Security and Rescue Rototics, Workshop, 2005.

自引率

0.00%

发文量