  We present a method for image-based crowd counting, one that can predict a
crowd density map together with the uncertainty values pertaining to the
predicted density map. To obtain prediction uncertainty, we model the crowd
density values using Gaussian distributions and develop a convolutional neural
network architecture to predict these distributions. A key advantage of our
method over existing crowd counting methods is its ability to quantify the
uncertainty of its predictions. We illustrate the benefits of knowing the
prediction uncertainty by developing a method to reduce the human annotation
effort needed to adapt counting networks to a new domain. We present sample
selection strategies which make use of the density and uncertainty of
predictions from the networks trained on one domain to select the informative
images from a target domain of interest to acquire human annotation. We show
that our sample selection strategy drastically reduces the amount of labeled
data from the target domain needed to adapt a counting network trained on a
source domain to the target domain. Empirically, the networks trained on
UCF-QNRF dataset can be adapted to surpass the performance of the previous
state-of-the-art results on NWPU dataset and Shanghaitech dataset using only
17$\%$ of the labeled training samples from the target domain.
