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In this paper we propose a technique to adapt a convolutional neural network(CNN) based object counter to additional visual domains and object types whilestill preserving the original counting function. Domain-specific normalisationand scaling operators are trained to allow the model to adjust to thestatistical distributions of the various visual domains. The developedadaptation technique is used to produce a singular patch-based countingregressor capable of counting various object types including people, vehicles,cell nuclei and wildlife. As part of this study a challenging new cell countingdataset in the context of tissue culture and patient diagnosis is constructed.This new collection, referred to as the Dublin Cell Counting (DCC) dataset, isthe first of its kind to be made available to the wider computer visioncommunity. State-of-the-art object counting performance is achieved in both theShanghaitech (parts A and B) and Penguins datasets while competitiveperformance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets,all using a shared counting model.

People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting