Reciprocal Distance Transform Maps for Crowd Counting and People Localization in Dense Crowd
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In this paper, we propose a novel map for dense crowd counting and people localization. Most crowd counting methods utilize convolution neural networks (CNN) to regress a density map, achieving significant progress recently. However, these regression-based methods are often unable to provide a precise location for each people, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map. To tackle this issue, we propose a novel Reciprocal Distance Transform (R-DT) map for crowd counting. Compared with the density maps, the R-DT maps accurately describe the people location, without overlap between nearby heads in dense regions. We simultaneously implement crowd counting and people localization with a simple network by replacing density maps with R-DT maps. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art localization-based methods in crowd counting and people localization tasks, achieving very competitive performance compared with the regression-based methods in counting tasks. In addition, the proposed method achieves a good generalization performance under cross dataset validation, which further verifies the effectiveness of the R-DT map. The code and models are available at https://github.com/dk-liang/RDTM.
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