  Multi-view crowd counting has been previously proposed to utilize
multi-cameras to extend the field-of-view of a single camera, capturing more
people in the scene, and improve counting performance for occluded people or
those in low resolution. However, the current multi-view paradigm trains and
tests on the same single scene and camera-views, which limits its practical
application. In this paper, we propose a cross-view cross-scene (CVCS)
multi-view crowd counting paradigm, where the training and testing occur on
different scenes with arbitrary camera layouts. To dynamically handle the
challenge of optimal view fusion under scene and camera layout change and
non-correspondence noise due to camera calibration errors or erroneous
features, we propose a CVCS model that attentively selects and fuses multiple
views together using camera layout geometry, and a noise view regularization
method to train the model to handle non-correspondence errors. We also generate
a large synthetic multi-camera crowd counting dataset with a large number of
scenes and camera views to capture many possible variations, which avoids the
difficulty of collecting and annotating such a large real dataset. We then test
our trained CVCS model on real multi-view counting datasets, by using
unsupervised domain transfer. The proposed CVCS model trained on synthetic data
outperforms the same model trained only on real data, and achieves promising
performance compared to fully supervised methods that train and test on the
same single scene.
