Performance evaluation of multi-camera visual tracking

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.

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