C. Premebida, Gonçalo Monteiro, U. Nunes, P. Peixoto
{"title":"A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking","authors":"C. Premebida, Gonçalo Monteiro, U. Nunes, P. Peixoto","doi":"10.1109/ITSC.2007.4357637","DOIUrl":null,"url":null,"abstract":"This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by in-vehicle Lidar and monocular vision is used. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian-sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.","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":"196","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Intelligent Transportation Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2007.4357637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 196
Abstract
This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by in-vehicle Lidar and monocular vision is used. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian-sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.