Performance Evaluation For Large-Scale Multi-Target Tracking Algorithms

The traditional method of applying the optimal sub-pattern assignment (OSPA) metric cannot fully evaluate multi-target tracking performance, as it does not account for phenomena such as track label switching, and track fragmentation. The OSPA((2)) has been proposed as a technique for applying the OSPA distance in a way that captures these effects, while retaining the properties of a true metric. In this paper, we demonstrate the behaviour of the OSPA((2)) on some numerical examples, discuss some of its advantages and limitations, and show that it is capable of being applied to performance evaluation of large-scale scenarios in the order of a thousand targets.