Name
Abstract | ||
|---|---|---|
Particle filters (PFs) are sequential Monte Carlo methods that use particle representation of state-space model to implement the recursive Bayesian filter for non-linear and non-Gaussian systems. Owing to this property, PFs have been extensively used for object tracking in recent years. Although PFs provide a robust object tracking framework, they suffer from shortcomings. Particle degeneracy and ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1049/iet-cvi.2016.0201 | IET Computer Vision |
Keywords | Field | DocType |
Monte Carlo methods,object tracking,optimisation,particle filtering (numerical methods),state estimation,state-space methods | Particle swarm optimization,Population,Computer vision,Heuristic,Particle filter,State-space representation,Cuckoo search,Video tracking,Artificial intelligence,Resampling,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 3 | 1751-9632 |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rajesh Rohilla | 1 | 1 | 0.70 |
| Vanshaj Sikri | 2 | 1 | 0.36 |
| Rajiv Kapoor | 3 | 286 | 20.94 |