  Density estimation is one of the most widely used methods for crowd counting
in which a deep learning model learns from head-annotated crowd images to
estimate crowd density in unseen images. Typically, the learning performance of
the model is highly impacted by the accuracy of the annotations and inaccurate
annotations may lead to localization and counting errors during prediction. A
significant amount of works exist on crowd counting using perfectly labelled
datasets but none of these explore the impact of annotation errors on the model
accuracy. In this paper, we investigate the impact of imperfect labels (both
noisy and missing labels) on crowd counting accuracy. We propose a system that
automatically generates imperfect labels using a deep learning model (called
annotator) which are then used to train a new crowd counting model (target
model). Our analysis on two crowd counting models and two benchmark datasets
shows that the proposed scheme achieves accuracy closer to that of the model
trained with perfect labels showing the robustness of crowd models to
annotation errors.
