Name
Abstract | ||
|---|---|---|
Comprehensive overview of the EM techniques with applications in joint tracking.Formulate the joint tracking problem in a united framework using EM method.Examples provide insights of the EM algorithm handling the problem of joint tracking.Discussions on open issues, ongoing research topics of the EM-based target tracking. Many target tracking problems can actually be cast as joint tracking problems where the underlying target state may only be observed via the relationship with a latent variable. In the presence of uncertainties in both observations and latent variable, which encapsulates the target tracking into a variational problem, the expectation-maximization (EM) method provides an iterative procedure under Bayesian inference framework to estimate the state of target in the process which minimizes the latent variable uncertainty. In this paper, we treat the joint tracking problem using a united framework under the EM method and provide a comprehensive overview of various EM approaches in joint tracking context from their necessity, benefits, and challenging viewpoints. Some examples on the EM application idea are presented. In addition, future research directions and open issues for using EM method in the joint tracking are given. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.inffus.2015.11.008 | Information Fusion |
Keywords | Field | DocType |
bayesian inference,expectation maximization | Data mining,Bayesian inference,Viewpoints,Expectation–maximization algorithm,Latent variable,Artificial intelligence,Information fusion,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
30 | C | 1566-2535 |
Citations | PageRank | References |
1 | 0.35 | 67 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Lan | 1 | 5 | 2.40 |
| xuezhi wang | 2 | 5 | 2.19 |
| quan pan | 3 | 239 | 17.11 |
| Feng Yang | 4 | 46 | 6.34 |
| Zengfu Wang | 5 | 1133 | 85.70 |
| Yan Liang | 6 | 154 | 23.49 |