Name
Abstract | ||
|---|---|---|
We describe a novel extension to the CONDENSATION algorithm for track- ing multiple objects of the same type. Previous extensions for multiple object tracking do not scale effectively to large numbers of objects. The new ap- proach - subordinated CONDENSATION - deals effectively with arbitrary numbers of objects in an efficient manner, providing a robust means of track- ing individual objects across heavily populated and cluttered scenes. The key innovation is the introduction of bindings (subordination) amongst particles which enables multiple occlusions to be handled in a natural way within the standard CONDENSATION framework. The effectiveness of the approach is demonstrated by tracking multiple animals of the same species in cluttered wildlife footage. |
Year | Venue | Field |
|---|---|---|
2002 | BMVC | Computer vision,Pattern recognition,Condensation,Computer science,Video tracking,Artificial intelligence,Condensation algorithm |
DocType | Citations | PageRank |
Conference | 21 | 2.83 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Tweed | 1 | 60 | 7.71 |
| Andrew Calway | 2 | 645 | 54.66 |