  Crowd counting has been studied for decades and a lot of works have achieved
good performance, especially the DNNs-based density map estimation methods.
Most existing crowd counting works focus on single-view counting, while few
works have studied multi-view counting for large and wide scenes, where
multiple cameras are used. Recently, an end-to-end multi-view crowd counting
method called multi-view multi-scale (MVMS) has been proposed, which fuses
multiple camera views using a CNN to predict a 2D scene-level density map on
the ground-plane. Unlike MVMS, we propose to solve the multi-view crowd
counting task through 3D feature fusion with 3D scene-level density maps,
instead of the 2D ground-plane ones. Compared to 2D fusion, the 3D fusion
extracts more information of the people along z-dimension (height), which helps
to solve the scale variations across multiple views. The 3D density maps still
preserve the 2D density maps property that the sum is the count, while also
providing 3D information about the crowd density. We also explore the
projection consistency among the 3D prediction and the ground-truth in the 2D
views to further enhance the counting performance. The proposed method is
tested on 3 multi-view counting datasets and achieves better or comparable
counting performance to the state-of-the-art.
