  In crowd counting, each training image contains multiple people, where each
person is annotated by a dot. Existing crowd counting methods need to use a
Gaussian to smooth each annotated dot or to estimate the likelihood of every
pixel given the annotated point. In this paper, we show that imposing Gaussians
to annotations hurts generalization performance. Instead, we propose to use
Distribution Matching for crowd COUNTing (DM-Count). In DM-Count, we use
Optimal Transport (OT) to measure the similarity between the normalized
predicted density map and the normalized ground truth density map. To stabilize
OT computation, we include a Total Variation loss in our model. We show that
the generalization error bound of DM-Count is tighter than that of the Gaussian
smoothed methods. In terms of Mean Absolute Error, DM-Count outperforms the
previous state-of-the-art methods by a large margin on two large-scale counting
datasets, UCF-QNRF and NWPU, and achieves the state-of-the-art results on the
ShanghaiTech and UCF-CC50 datasets. DM-Count reduced the error of the
state-of-the-art published result by approximately 16%. Code is available at
https://github.com/cvlab-stonybrook/DM-Count.
