Name
Abstract | ||
|---|---|---|
In this paper we will consider several algorithms for tracking closely spaced objects. In particular we will concentrate on various particle filter implementations. One particular problem when using a joint multi target particle filter is the so-called mixed labelling problem. This problem amounts to the fact that different particles will have a different labelling w.r.t. target identity. The combination of the mixed labelling problem and naive or straightforward track extraction leads to performance degradation. This will be illustrated and alternative methods to alleviate this effect will be presented. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICIF.2007.4407983 | Quebec, Que. |
Keywords | Field | DocType |
object detection,particle filtering (numerical methods),target tracking,closely spaced objects,closely spaced target tracking,mixed labelling problem,multi target particle filter,particle filters,track extraction,Mixed labelling,Multi-target tracking,Particle Filter | Computer vision,Object detection,Kinematics,Multi target tracking,Computer science,Particle filter,Kalman filter,Labelling,Artificial intelligence,Probability density function | Conference |
ISBN | Citations | PageRank |
978-0-662-45804-3 | 9 | 1.78 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mats Ekman | 1 | 16 | 2.49 |
| Egils Sviestins | 2 | 12 | 2.93 |
| Lars Sjöberg | 3 | 9 | 1.78 |