  We propose a novel crowd counting model that maps a given crowd scene to its
density. Crowd analysis is compounded by myriad of factors like inter-occlusion
between people due to extreme crowding, high similarity of appearance between
people and background elements, and large variability of camera view-points.
Current state-of-the art approaches tackle these factors by using multi-scale
CNN architectures, recurrent networks and late fusion of features from
multi-column CNN with different receptive fields. We propose switching
convolutional neural network that leverages variation of crowd density within
an image to improve the accuracy and localization of the predicted crowd count.
Patches from a grid within a crowd scene are relayed to independent CNN
regressors based on crowd count prediction quality of the CNN established
during training. The independent CNN regressors are designed to have different
receptive fields and a switch classifier is trained to relay the crowd scene
patch to the best CNN regressor. We perform extensive experiments on all major
crowd counting datasets and evidence better performance compared to current
state-of-the-art methods. We provide interpretable representations of the
multichotomy of space of crowd scene patches inferred from the switch. It is
observed that the switch relays an image patch to a particular CNN column based
on density of crowd.
