Pose estimation and tracking using multivariate regression

被引：49

作者：

Thayananthan, Arasanathan ^{[2
]}

Navaratnam, Ramanan ^{[2
]}

Stenger, Bjoern ^{[1
]}

Torr, Philip H. S. ^{[3
]}

Cipolla, Roberto ^{[2
]}

机构：

[1] Toshiba Cambridge Res Lab, Cambridge CB4 0GZ, England

[2] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England

[3] Oxford Brookes Univ, Dept Comp, Oxford OX33 1HX, England

来源：

PATTERN RECOGNITION LETTERS | 2008年 / 29卷 / 09期

关键词：

regression; relevance vector machines; tracking; articulated motion;

D O I：

10.1016/j.patrec.2008.02.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an extension of the relevance vector machine (RVM) algorithm to multivariate regression. This allows the application to the task of estimating the pose of an articulated object from a single camera. RVMs are used to learn a one-to-many mapping from image features to state space, thereby being able to handle pose ambiguity. (c) 2008 Elsevier B.V. All rights reserved.

引用

页码：1302 / 1310

页数：9

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