Spooky effect in optimal OSPA estimation and how GOSPA solves it
This addresses a fundamental problem in multi-target tracking for applications like surveillance or robotics, though it appears incremental as it builds on existing metrics.
The paper identifies a 'spooky effect' in optimal estimation with the OSPA metric, where changes in one target's probability can disrupt estimation of others, and shows that the GOSPA metric avoids this issue by penalizing localization errors, false targets, and missed targets.
In this paper, we show the spooky effect at a distance that arises in optimal estimation of multiple targets with the optimal sub-pattern assignment (OSPA) metric. This effect refers to the fact that if we have several independent potential targets at distant locations, a change in the probability of existence of one of them can completely change the optimal estimation of the rest of the potential targets. As opposed to OSPA, the generalised OSPA (GOSPA) metric ($α=2$) penalises localisation errors for properly detected targets, false targets and missed targets. As a consequence, optimal GOSPA estimation aims to lower the number of false and missed targets, as well as the localisation error for properly detected targets, and avoids the spooky effect.