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This paper proposes a crowd counting method. Crowd counting is difficultbecause of large appearance changes of a target which caused by density andscale changes. Conventional crowd counting methods generally utilize onepredictor (e,g., regression and multi-class classifier). However, such only onepredictor can not count targets with large appearance changes well. In thispaper, we propose to predict the number of targets using multiple CNNsspecialized to a specific appearance, and those CNNs are adaptively selectedaccording to the appearance of a test image. By integrating the selected CNNs,the proposed method has the robustness to large appearance changes. Inexperiments, we confirm that the proposed method can count crowd with lowercounting error than a CNN and integration of CNNs with fixed weights. Moreover,we confirm that each predictor automatically specialized to a specificappearance.

Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting