  Video crowd localization is a crucial yet challenging task, which aims to
estimate exact locations of human heads in the given crowded videos. To model
spatial-temporal dependencies of human mobility, we propose a multi-focus
Gaussian neighborhood attention (GNA), which can effectively exploit long-range
correspondences while maintaining the spatial topological structure of the
input videos. In particular, our GNA can also capture the scale variation of
human heads well using the equipped multi-focus mechanism. Based on the
multi-focus GNA, we develop a unified neural network called GNANet to
accurately locate head centers in video clips by fully aggregating
spatial-temporal information via a scene modeling module and a context
cross-attention module. Moreover, to facilitate future researches in this
field, we introduce a large-scale crowd video benchmark named VSCrowd, which
consists of 60K+ frames captured in various surveillance scenarios and 2M+ head
annotations. Finally, we conduct extensive experiments on three datasets
including our SenseCrowd, and the experiment results show that the proposed
method is capable to achieve state-of-the-art performance for both video crowd
localization and counting.
