{"title":"城市灾害现场的受害者检测和定位","authors":"Bhuman Soni, A. Sowmya","doi":"10.1109/ROBIO.2013.6739786","DOIUrl":null,"url":null,"abstract":"In this study, we model the disaster victim detection problem as a sub-problem of a larger casualty assessment problem, and propose a framework to solve it. The framework of algorithm independent components contains a victim detector, detection history component and a human robot interaction component that presents information obtained by the robot in a meaningful manner. The algorithm independence of the victim detector component is demonstrated by experiments conducted in a simulated disaster scenario using a simple body parts detector that uses HOG features with an SVM classifier, and the state-of-the-art DPM body parts detector. A FastSLAM based mapping component is used to keep track of unique detections and the information is presented via a tab based user interface. The experiments demonstrate the effectiveness of the framework components with the rescue robot correctly identifying the victims and presenting a map of the disaster location with victim markers.","PeriodicalId":434960,"journal":{"name":"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Victim detection and localisation in an urban disaster site\",\"authors\":\"Bhuman Soni, A. Sowmya\",\"doi\":\"10.1109/ROBIO.2013.6739786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we model the disaster victim detection problem as a sub-problem of a larger casualty assessment problem, and propose a framework to solve it. The framework of algorithm independent components contains a victim detector, detection history component and a human robot interaction component that presents information obtained by the robot in a meaningful manner. The algorithm independence of the victim detector component is demonstrated by experiments conducted in a simulated disaster scenario using a simple body parts detector that uses HOG features with an SVM classifier, and the state-of-the-art DPM body parts detector. A FastSLAM based mapping component is used to keep track of unique detections and the information is presented via a tab based user interface. The experiments demonstrate the effectiveness of the framework components with the rescue robot correctly identifying the victims and presenting a map of the disaster location with victim markers.\",\"PeriodicalId\":434960,\"journal\":{\"name\":\"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROBIO.2013.6739786\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBIO.2013.6739786","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Victim detection and localisation in an urban disaster site
In this study, we model the disaster victim detection problem as a sub-problem of a larger casualty assessment problem, and propose a framework to solve it. The framework of algorithm independent components contains a victim detector, detection history component and a human robot interaction component that presents information obtained by the robot in a meaningful manner. The algorithm independence of the victim detector component is demonstrated by experiments conducted in a simulated disaster scenario using a simple body parts detector that uses HOG features with an SVM classifier, and the state-of-the-art DPM body parts detector. A FastSLAM based mapping component is used to keep track of unique detections and the information is presented via a tab based user interface. The experiments demonstrate the effectiveness of the framework components with the rescue robot correctly identifying the victims and presenting a map of the disaster location with victim markers.