{"title":"Fast Approximated SIFT Applied in Moving Objects Detection","authors":"Wei Tang, Zhaoshun Wang","doi":"10.1109/CCPR.2008.47","DOIUrl":null,"url":null,"abstract":"To make the moving object detection faster and more reliable, in this paper we present a novel method based on fast approximated SIFT descriptor. The main idea is to compute the feature descriptor of a key-point using the integral histogram of the surrounding squared region. The feature descriptor could be further used in the feature matching between two sequential frames in the image sequence. When involved in calculating hundreds of feature descriptors, this method is profitable as it reduced computational cost, accelerated the computational speed while still maintained a fairly stable matching performance compared with the traditional SIFT descriptor. The experimental results showed that it was nearly three times faster than before and was able to meet more restrict real-time requirements.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"155 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Chinese Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCPR.2008.47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
To make the moving object detection faster and more reliable, in this paper we present a novel method based on fast approximated SIFT descriptor. The main idea is to compute the feature descriptor of a key-point using the integral histogram of the surrounding squared region. The feature descriptor could be further used in the feature matching between two sequential frames in the image sequence. When involved in calculating hundreds of feature descriptors, this method is profitable as it reduced computational cost, accelerated the computational speed while still maintained a fairly stable matching performance compared with the traditional SIFT descriptor. The experimental results showed that it was nearly three times faster than before and was able to meet more restrict real-time requirements.