Name
Abstract | ||
|---|---|---|
In this paper, we formulate the feature clustering problem for vehicle detection and tracking as a general MAP problem and solve it using MCMC. The proposed approach exhibits two advantages over existing methods: general Bayesian model can handle arbitrary objective functions and MCMC guarantees global optimal solution. Our algorithm is validated on real-world traffic video sequences, and is shown to outperform the state-of-the-art approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5413526 | ICIP |
Keywords | Field | DocType |
feature clustering,state-of-the-art approach,general bayesian model,object detection,vehicle detection,bayes methods,traffic engineering computing,objective functions,general map problem,traffic video sequences,real-world traffic video sequence,arbitrary objective function,markov chain monte carlo,global optimal solution,clustering methods,tracking,image sequences,monte carlo methods,vehicle tracking,road traffic surveillance,map estimation,road traffic,markov processes,indexing terms,shape,bayesian methods,feature extraction,clustering algorithms,objective function,monte carlo method,trajectory,bayesian model,global optimization | Computer vision,Object detection,Markov process,Bayesian inference,Pattern recognition,Markov chain Monte Carlo,Computer science,Feature extraction,Artificial intelligence,Cluster analysis,Trajectory,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 7 |
PageRank | References | Authors |
0.55 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Yang | 1 | 225 | 19.00 |
| Yang Wang | 2 | 108 | 12.95 |
| Getian Ye | 3 | 81 | 9.47 |
| Arcot Sowmya | 4 | 319 | 60.05 |
| Bang Zhang | 5 | 111 | 12.40 |
| Jie Xu | 6 | 64 | 8.22 |