  Crowd counting in single-view images has achieved outstanding performance on
existing counting datasets. However, single-view counting is not applicable to
large and wide scenes (e.g., public parks, long subway platforms, or event
spaces) because a single camera cannot capture the whole scene in adequate
detail for counting, e.g., when the scene is too large to fit into the
field-of-view of the camera, too long so that the resolution is too low on
faraway crowds, or when there are too many large objects that occlude large
portions of the crowd. Therefore, to solve the wide-area counting task requires
multiple cameras with overlapping fields-of-view. In this paper, we propose a
deep neural network framework for multi-view crowd counting, which fuses
information from multiple camera views to predict a scene-level density map on
the ground-plane of the 3D world. We consider three versions of the fusion
framework: the late fusion model fuses camera-view density map; the naive early
fusion model fuses camera-view feature maps; and the multi-view multi-scale
early fusion model ensures that features aligned to the same ground-plane point
have consistent scales. A rotation selection module further ensures consistent
rotation alignment of the features. We test our 3 fusion models on 3 multi-view
counting datasets, PETS2009, DukeMTMC, and a newly collected multi-view
counting dataset containing a crowded street intersection. Our methods achieve
state-of-the-art results compared to other multi-view counting baselines.
