Name
Abstract | ||
|---|---|---|
This paper presents a method for 3D model-based tracking of colored ob- jects using a sampling methodology. The problem is formulated in a Monte Carlo filtering approach, whereby the state of an object is re presented by a set of hypotheses. The main originality of this work is an observation model consisting in the comparison of the color information in some sam- pling points around the target's hypothetical edges. On the contrary to ex- isting approaches the method does not need to explicitly compute edges in the video stream, thus dealing well with optical or motion blur. The method does not require the projection of the full 3D object on the image, but just of some selected points around the target's boundaries. This a llows a flexible and modular architecture illustrated by experiments performed with different objects (balls and boxes), camera models (perspective, catadioptric, dioptric) and tracking methodologies (particle and Kalman filtering) . |
Year | Venue | Keywords |
|---|---|---|
2008 | BMVC | kalman filter |
Field | DocType | Citations |
Computer vision,Monte Carlo method,Colored,Computer science,Ball (bearing),Motion blur,Filter (signal processing),Kalman filter,Sampling (statistics),Artificial intelligence,Catadioptric system | Conference | 5 |
PageRank | References | Authors |
0.71 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matteo Taiana | 1 | 39 | 3.68 |
| Jacinto C. Nascimento | 2 | 396 | 40.94 |
| José António Gaspar | 3 | 26 | 4.89 |
| Alexandre Bernardino | 4 | 710 | 78.77 |