{"title":"A relaxation method to articulated trajectory reconstruction from monocular image sequence","authors":"Bo Li, Yuchao Dai, Mingyi He, A. Hengel","doi":"10.1109/ChinaSIP.2014.6889270","DOIUrl":null,"url":null,"abstract":"In this paper, we present a novel method for articulated trajectory reconstruction from a monocular image sequence. We propose a relaxation-based objective function, which utilises both smoothness and geometric constraints, posing articulated trajectory reconstruction as a non-linear optimization problem. The main advantage of this approach is that it remains the re-constructive power of the original algorithm, while improving its robustness to the inevitable noise in the data. Furthermore, we present an effective approach to estimating the parameters of our objective function. Experimental results on the CMU motion capture dataset show that our proposed algorithm is effective.","PeriodicalId":248977,"journal":{"name":"2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ChinaSIP.2014.6889270","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
In this paper, we present a novel method for articulated trajectory reconstruction from a monocular image sequence. We propose a relaxation-based objective function, which utilises both smoothness and geometric constraints, posing articulated trajectory reconstruction as a non-linear optimization problem. The main advantage of this approach is that it remains the re-constructive power of the original algorithm, while improving its robustness to the inevitable noise in the data. Furthermore, we present an effective approach to estimating the parameters of our objective function. Experimental results on the CMU motion capture dataset show that our proposed algorithm is effective.