Published 2012
| Version v1
Publication
Performance evaluation of multi-camera visual tracking
Contributors
Description
Main drawbacks in single-camera multi-target visual tracking can be partially removed by increasing the amount
of information gathered on the scene, i.e. by adding cameras.
By adopting such a multi-camera approach, multiple sensors
cooperate for overall scene understanding. However, new issues
arise such as data association and data fusion. This work
addresses the issue of evaluating the performance of a multicamera tracking algorithm based on Rao-Blackwellized Monte
Carlo data association (RBMCDA) on real data. For this purpose,
a new metric based on three performance indexes is developed.
Additional details
Identifiers
- URL
- http://hdl.handle.net/11567/382759
- URN
- urn:oai:iris.unige.it:11567/382759