{"title":"3D vision sensing for improved pedestrian safety","authors":"G. Grubb, A. Zelinsky, L. Nilsson, M. Rilbe","doi":"10.1109/IVS.2004.1336349","DOIUrl":null,"url":null,"abstract":"Pedestrian-vehicle accidents account for the second largest source of automotive related fatality and injury worldwide. This paper presents a system which detects and tracks pedestrians in realtime for use with automotive pedestrian protection systems (PPS) aimed at reducing such pedestrian-vehicle related injury. The system is based on a passive stereo vision configuration which segments a scene into 3D objects, classifies each object as pedestrian/non-pedestrian and finally tracks the pedestrian in 3D. Our system was implemented and tested on a Volvo test vehicle. Strong results for the system were obtained over a range of simple and complex environments, with average positive and false positive detection rates of 83.5% and 0.4%, respectively.","PeriodicalId":296386,"journal":{"name":"IEEE Intelligent Vehicles Symposium, 2004","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"119","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Intelligent Vehicles Symposium, 2004","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2004.1336349","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 119
Abstract
Pedestrian-vehicle accidents account for the second largest source of automotive related fatality and injury worldwide. This paper presents a system which detects and tracks pedestrians in realtime for use with automotive pedestrian protection systems (PPS) aimed at reducing such pedestrian-vehicle related injury. The system is based on a passive stereo vision configuration which segments a scene into 3D objects, classifies each object as pedestrian/non-pedestrian and finally tracks the pedestrian in 3D. Our system was implemented and tested on a Volvo test vehicle. Strong results for the system were obtained over a range of simple and complex environments, with average positive and false positive detection rates of 83.5% and 0.4%, respectively.
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