Name
Abstract | ||
|---|---|---|
A vision system is demonstrated that adaptively allocates computational resources over multiple cues to robustly track a target in 3D. The system uses a particle filter to maintain multiple hypotheses of the target location. Bayesian probability theory provides the framework for sensor fusion, and resource scheduling is used to intelli-gently allocate the limited computational resources available across the suite of cues. The system is shown to track a person in 3D space moving in a cluttered environment. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/AFGR.2002.1004164 | FGR |
Keywords | Field | DocType |
Bayes methods,computer vision,image motion analysis,probability,resource allocation,scheduling,sensor fusion,tracking,3D target tracking,Bayesian probability theory,adaptive fusion architecture,cluttered environment,computational resource allocation,computer vision system,multiple cues,particle filter,person tracking,resource scheduling,sensor fusion | Resource management,Computer vision,Machine vision,Suite,Scheduling (computing),Computer science,Particle filter,Sensor fusion,Resource allocation,Artificial intelligence,Bayesian probability | Conference |
ISBN | Citations | PageRank |
0-7695-1602-5 | 33 | 4.89 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gareth Loy | 1 | 627 | 42.88 |
| Luke Fletcher | 2 | 340 | 32.95 |
| Nicholas Apostoloff | 3 | 143 | 13.69 |
| Alexander Zelinsky | 4 | 1144 | 124.18 |