M. Bertozzi, A. Broggi, M. Rose, M. Felisa, A. Rakotomamonjy, F. Suard
{"title":"A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier","authors":"M. Bertozzi, A. Broggi, M. Rose, M. Felisa, A. Rakotomamonjy, F. Suard","doi":"10.1109/ITSC.2007.4357692","DOIUrl":null,"url":null,"abstract":"This paper details filtering subsystem for a tetra-vision based pedestrian detection system. The complete system is based on the use of both visible and far infrared cameras; in an initial phase it produces a list of areas of attention in the images which can contain pedestrians. This list is furtherly refined using symmetry-based assumptions. Then, this results is fed to a number of independent validators that evaluate the presence of human shapes inside the areas of attention. Histogram of oriented gradients and Support Vector Machines are used as a filter and demonstrated to be able to successfully classify up to 91% of pedestrians in the areas of attention.","PeriodicalId":211095,"journal":{"name":"2007 IEEE Intelligent Transportation Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2007-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"123","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Intelligent Transportation Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2007.4357692","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 123
Abstract
This paper details filtering subsystem for a tetra-vision based pedestrian detection system. The complete system is based on the use of both visible and far infrared cameras; in an initial phase it produces a list of areas of attention in the images which can contain pedestrians. This list is furtherly refined using symmetry-based assumptions. Then, this results is fed to a number of independent validators that evaluate the presence of human shapes inside the areas of attention. Histogram of oriented gradients and Support Vector Machines are used as a filter and demonstrated to be able to successfully classify up to 91% of pedestrians in the areas of attention.