  In this paper, we propose a simple yet effective crowd counting and
localization network named SCALNet. Unlike most existing works that separate
the counting and localization tasks, we consider those tasks as a pixel-wise
dense prediction problem and integrate them into an end-to-end framework.
Specifically, for crowd counting, we adopt a counting head supervised by the
Mean Square Error (MSE) loss. For crowd localization, the key insight is to
recognize the keypoint of people, i.e., the center point of heads. We propose a
localization head to distinguish dense crowds trained by two loss functions,
i.e., Negative-Suppressed Focal (NSF) loss and False-Positive (FP) loss, which
balances the positive/negative examples and handles the false-positive
predictions. Experiments on the recent and large-scale benchmark, NWPU-Crowd,
show that our approach outperforms the state-of-the-art methods by more than 5%
and 10% improvement in crowd localization and counting tasks, respectively. The
code is publicly available at https://github.com/WangyiNTU/SCALNet.
