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- W3165961384 abstract "Crowd counting is getting more and more attention. More and more collective activities, such as the Olympics Games and the World Expo, are also important to control the crowd number. In this paper, we address the problem of crowd counting in the crowded scene. Our model accurately estimated the count of people in the crowded scene. Firstly, we proposed a novel and simple convolutional neural network, called Global Counting CNN (GCCNN). The GCCNN can learn a mapping, transforms the appearance of image patches to estimated density maps. Secondly, The Local to Global counting CNN (LGCCNN), calculating the density map from local to global. Stiching the local patches constrains the final density map of the larger area, which make up for the difference values in the perspective map. In general, it makes the final density map more accurate. The dataset we used is a set of public dataset, which are WorldExpo’10 dataset, Shanghaitech dataset, the UCF_CC_50 dataset and the UCSD dataset. The experiments have proved our method achieves the state-of-the-art result over other algorithms." @default.
- W3165961384 created "2021-06-22" @default.
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- W3165961384 date "2020-11-11" @default.
- W3165961384 modified "2023-09-27" @default.
- W3165961384 title "Arbitrary Perspective Crowd Counting via Local to Global Algorithm" @default.
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- W3165961384 doi "https://doi.org/10.1007/978-3-030-56178-9_3" @default.
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