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Arbitrary perspective crowd counting via local to global algorithm
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DOI：10.1007/s11042-020-08888-5.png)
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Abstract
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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 makes 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.
Keywords:
Crowd density map
Convolutional neural network
Perspective distortion
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