  Automated counting of people in crowd images is a challenging task. The major
difficulty stems from the large diversity in the way people appear in crowds.
In fact, features available for crowd discrimination largely depend on the
crowd density to the extent that people are only seen as blobs in a highly
dense scene. We tackle this problem with a growing CNN which can progressively
increase its capacity to account for the wide variability seen in crowd scenes.
Our model starts from a base CNN density regressor, which is trained in
equivalence on all types of crowd images. In order to adapt with the huge
diversity, we create two child regressors which are exact copies of the base
CNN. A differential training procedure divides the dataset into two clusters
and fine-tunes the child networks on their respective specialties.
Consequently, without any hand-crafted criteria for forming specialties, the
child regressors become experts on certain types of crowds. The child networks
are again split recursively, creating two experts at every division. This
hierarchical training leads to a CNN tree, where the child regressors are more
fine experts than any of their parents. The leaf nodes are taken as the final
experts and a classifier network is then trained to predict the correct
specialty for a given test image patch. The proposed model achieves higher
count accuracy on major crowd datasets. Further, we analyse the characteristics
of specialties mined automatically by our method.
