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Crowd counting is a challenging task due to the large variations in crowddistributions. Previous methods tend to tackle the whole image with a singlefixed structure, which is unable to handle diverse complicated scenes withdifferent crowd densities. Hence, we propose the Adaptive Capacity Multi-scaleconvolutional neural networks (ACM-CNN), a novel crowd counting approach whichcan assign different capacities to different portions of the input. Theintuition is that the model should focus on important regions of the inputimage and optimize its capacity allocation conditioning on the crowd intensivedegree. ACM-CNN consists of three types of modules: a coarse network, a finenetwork, and a smooth network. The coarse network is used to explore the areasthat need to be focused via count attention mechanism, and generate a roughfeature map. Then the fine network processes the areas of interest into a finefeature map. To alleviate the sense of division caused by fusion, the smoothnetwork is designed to combine two feature maps organically to producehigh-quality density maps. Extensive experiments are conducted on fivemainstream datasets. The results demonstrate the effectiveness of the proposedmodel for both density estimation and crowd counting tasks.

Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs