  Localizing individuals in crowds is more in accordance with the practical
demands of subsequent high-level crowd analysis tasks than simply counting.
However, existing localization based methods relying on intermediate
representations (\textit{i.e.}, density maps or pseudo boxes) serving as
learning targets are counter-intuitive and error-prone. In this paper, we
propose a purely point-based framework for joint crowd counting and individual
localization. For this framework, instead of merely reporting the absolute
counting error at image level, we propose a new metric, called density
Normalized Average Precision (nAP), to provide more comprehensive and more
precise performance evaluation. Moreover, we design an intuitive solution under
this framework, which is called Point to Point Network (P2PNet). P2PNet
discards superfluous steps and directly predicts a set of point proposals to
represent heads in an image, being consistent with the human annotation
results. By thorough analysis, we reveal the key step towards implementing such
a novel idea is to assign optimal learning targets for these proposals.
Therefore, we propose to conduct this crucial association in an one-to-one
matching manner using the Hungarian algorithm. The P2PNet not only
significantly surpasses state-of-the-art methods on popular counting
benchmarks, but also achieves promising localization accuracy. The codes will
be available at: https://github.com/TencentYoutuResearch/CrowdCounting-P2PNet.
