Evaluating Crowd Density Estimators via Their Uncertainty Bounds
This work addresses the need for reliable uncertainty estimation in crowd monitoring, but it is incremental as it applies an existing theoretical framework to a specific domain.
The paper tackled the problem of evaluating crowd density estimators by using Belief Function Theory to provide uncertainty bounds, enabling comparison of multi-scale performance and characterization of reliability for crowd monitoring applications.
In this work, we use the Belief Function Theory which extends the probabilistic framework in order to provide uncertainty bounds to different categories of crowd density estimators. Our method allows us to compare the multi-scale performance of the estimators, and also to characterize their reliability for crowd monitoring applications requiring varying degrees of prudence.