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

Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN