Name
Title | ||
|---|---|---|
Moving targets labeling and correspondence over multi-camera surveillance system based on Markov network |
Abstract | ||
|---|---|---|
In this paper, we propose an efficient way to simultaneously label and map targets over a multi-camera surveillance system. In the system, we first fuse the detection results from multiple cameras into a posterior distribution. This distribution indicates the likelihood of having some moving targets on the ground plane. Based on the distribution, isolated targets, together with their 3-D positions, are identified in a sample-based manner, which combines Markov Chain Monte Carlo (MCMC), and Mean-Shift clustering. The induced 3-D scene information is further inputted into a 3-layer Bayesian hierarchical framework (BHF), which adopts a Markov network to deal with the object labeling and correspondence problems. In principle, labeling and correspondence are regarded as a unified optimal problem subject to 3-D scene prior, image color similarity, and detection results. The experiments show that accurate results can be gotten even under situations with severe occlusion. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICME.2009.5202730 | ICME |
Keywords | Field | DocType |
graphical model,correspondence problem,posterior distribution,labeling,graphical models,markov processes,mathematical model,pixel,monte carlo methods,mean shift,optimization problem,bayesian methods,markov chain monte carlo,mean shift clustering | Object detection,Computer vision,Markov process,Markov chain Monte Carlo,Pattern recognition,Computer science,Markov chain,Posterior probability,Artificial intelligence,Mean-shift,Graphical model,Cluster analysis | Conference |
ISSN | Citations | PageRank |
1945-7871 | 1 | 0.35 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-chun Huang | 1 | 135 | 9.63 |
| Sheng-Jyh Wang | 2 | 202 | 23.46 |