Learn to Scale: Generating Multipolar Normalized Density Map for Crowd Counting
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Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels. Existing approaches mainly suffer from the extreme density variances. Such density pattern shift poses challenges even for multi-scale model ensembling. In this paper, we propose a simple yet effective approach to tackle this problem. First, a patch-level density map is extracted by a density estimation model, and is further grouped into several density levels which are determined over full datasets. Second, each patch density map is automatically normalized by an online center learning strategy with a multipolar center loss (MPCL). Such a design can significantly condense the density distribution into several clusters, and enable that the density variance can be learned by a single model. Extensive experiments show the best accuracy of the proposed framework in several crowd counting datasets, with relative accuracy gains of 4.2 14.3 A, Part B, UCF_CC_50, UCF-QNRF dataset, respectively.
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