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Crowd counting has been studied for decades and a lot of works have achievedgood performance, especially the DNNs-based density map estimation methods.Most existing crowd counting works focus on single-view counting, while fewworks have studied multi-view counting for large and wide scenes, wheremultiple cameras are used. Recently, an end-to-end multi-view crowd countingmethod called multi-view multi-scale (MVMS) has been proposed, which fusesmultiple camera views using a CNN to predict a 2D scene-level density map onthe ground-plane. Unlike MVMS, we propose to solve the multi-view crowdcounting task through 3D feature fusion with 3D scene-level density maps,instead of the 2D ground-plane ones. Compared to 2D fusion, the 3D fusionextracts more information of the people along z-dimension (height), which helpsto solve the scale variations across multiple views. The 3D density maps stillpreserve the 2D density maps property that the sum is the count, while alsoproviding 3D information about the crowd density. We also explore theprojection consistency among the 3D prediction and the ground-truth in the 2Dviews to further enhance the counting performance. The proposed method istested on 3 multi-view counting datasets and achieves better or comparablecounting performance to the state-of-the-art.

3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels