Vincenzo Carletti, P. Foggia, Antonio Greco, Alessia Saggese, M. Vento
{"title":"Automatic detection of long term parked cars","authors":"Vincenzo Carletti, P. Foggia, Antonio Greco, Alessia Saggese, M. Vento","doi":"10.1109/AVSS.2015.7301722","DOIUrl":null,"url":null,"abstract":"The detection of illegal roadside parking is becoming more and more interesting in the field of intelligent transportation systems, since it may cause traffic congestion or accidents. In this paper we propose a method able to analyze videos acquired by traditional surveillance cameras and to automatically detect the vehicles stopped in a forbidden area. Two main contributions have been introduced: first, spatio temporal information related to the stopped vehicles are encoded by a heat map; second, the background is not updated by evaluating the movement of the vehicle in a single time instant, but instead the whole movement of the vehicles, encoded into the heat map, is taken into account. Two widely adopted datasets, namely the iLids and the PETS 2000, have been used to experimentally evaluate the proposed approach and the results achieved, compared with state of the art methodologies, confirm its effectiveness.","PeriodicalId":101864,"journal":{"name":"2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AVSS.2015.7301722","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
The detection of illegal roadside parking is becoming more and more interesting in the field of intelligent transportation systems, since it may cause traffic congestion or accidents. In this paper we propose a method able to analyze videos acquired by traditional surveillance cameras and to automatically detect the vehicles stopped in a forbidden area. Two main contributions have been introduced: first, spatio temporal information related to the stopped vehicles are encoded by a heat map; second, the background is not updated by evaluating the movement of the vehicle in a single time instant, but instead the whole movement of the vehicles, encoded into the heat map, is taken into account. Two widely adopted datasets, namely the iLids and the PETS 2000, have been used to experimentally evaluate the proposed approach and the results achieved, compared with state of the art methodologies, confirm its effectiveness.