Name
Abstract | ||
|---|---|---|
In this paper an approach is described to estimate 3D pose using a part based stochastic method. A proposed representation of the human body is explored defined over joints that employs full conditional models learnt between connected joints. This representation is compared against a popular alternative defined over parts using approximated limb conditionals. It is shown that using full limb conditionals results in a model that is far more representative of the original training data. Furthermore, it is demonstrated that Expectation Maximization is suitable for estimating 3D pose and better convergence is achieved when using full limb conditionals. To demonstrate the efficacy of the proposed method it is applied to the domain of 3D pose estimation using a single monocular image. Quantitative results are provided using the HumanEva dataset which confirm that the proposed method outperforms that of the competing part based model. In this work just a single model is learnt to represent all actions contained in the dataset which is applied to all subjects viewed from differing angles. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14061-7_7 | AMDO |
Keywords | Field | DocType |
single model,approximated limb conditional,stochastic method,full conditional models learnt,full limb conditional,expectation maximization,competing part,humaneva dataset,stochastic search,full limb conditionals result,proposed representation,human body,3d pose estimation | Training set,Convergence (routing),Computer vision,Expectation–maximization algorithm,Computer science,3D pose estimation,Monocular image,Artificial intelligence,Discriminative model,Machine learning | Conference |
Volume | ISSN | ISBN |
6169 | 0302-9743 | 3-642-14060-2 |
Citations | PageRank | References |
3 | 0.40 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ben Daubney | 1 | 78 | 5.71 |
| Xianghua Xie | 2 | 383 | 37.13 |