  In crowd counting datasets, each person is annotated by a point, which is
usually the center of the head. And the task is to estimate the total count in
a crowd scene. Most of the state-of-the-art methods are based on density map
estimation, which convert the sparse point annotations into a "ground truth"
density map through a Gaussian kernel, and then use it as the learning target
to train a density map estimator. However, such a "ground-truth" density map is
imperfect due to occlusions, perspective effects, variations in object shapes,
etc. On the contrary, we propose \emph{Bayesian loss}, a novel loss function
which constructs a density contribution probability model from the point
annotations. Instead of constraining the value at every pixel in the density
map, the proposed training loss adopts a more reliable supervision on the count
expectation at each annotated point. Without bells and whistles, the loss
function makes substantial improvements over the baseline loss on all tested
datasets. Moreover, our proposed loss function equipped with a standard
backbone network, without using any external detectors or multi-scale
architectures, plays favourably against the state of the arts. Our method
outperforms previous best approaches by a large margin on the latest and
largest UCF-QNRF dataset. The source code is available at
\url{https://github.com/ZhihengCV/Baysian-Crowd-Counting}.

Computer Vision and Pattern Recognition