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This paper proposes a novel approach for crowd counting in low to highdensity scenarios in static images. Current approaches cannot handle huge crowddiversity well and thus perform poorly in extreme cases, where the crowddensity in different regions of an image is either too low or too high, leadingto crowd underestimation or overestimation. The proposed solution is based onthe observation that detecting and handling such extreme cases in a specializedway leads to better crowd estimation. Additionally, existing methods find ithard to differentiate between the actual crowd and the cluttered backgroundregions, resulting in further count overestimation. To address these issues, wepropose a simple yet effective modular approach, where an input image is firstsubdivided into fixed-size patches and then fed to a four-way classificationmodule labeling each image patch as low, medium, high-dense or no-crowd. Thismodule also provides a count for each label, which is then analyzed via aspecifically devised novel decision module to decide whether the image belongsto any of the two extreme cases (very low or very high density) or a normalcase. Images, specified as high- or low-density extreme or a normal case, passthrough dedicated zooming or normal patch-making blocks respectively beforerouting to the regressor in the form of fixed-size patches for crowd estimate.Extensive experimental evaluations demonstrate that the proposed approachoutperforms the state-of-the-art methods on four benchmarks under most of theevaluation criteria.

ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images