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In real-world crowd counting applications, the crowd densities vary greatlyin spatial and temporal domains. A detection based counting method willestimate crowds accurately in low density scenes, while its reliability incongested areas is downgraded. A regression based approach, on the other hand,captures the general density information in crowded regions. Without knowingthe location of each person, it tends to overestimate the count in low densityareas. Thus, exclusively using either one of them is not sufficient to handleall kinds of scenes with varying densities. To address this issue, a novelend-to-end crowd counting framework, named DecideNet (DEteCtIon and DensityEstimation Network) is proposed. It can adaptively decide the appropriatecounting mode for different locations on the image based on its real densityconditions. DecideNet starts with estimating the crowd density by generatingdetection and regression based density maps separately. To capture inevitablevariation in densities, it incorporates an attention module, meant toadaptively assess the reliability of the two types of estimations. The finalcrowd counts are obtained with the guidance of the attention module to adoptsuitable estimations from the two kinds of density maps. Experimental resultsshow that our method achieves state-of-the-art performance on three challengingcrowd counting datasets.

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation