Feng Min
ABSTRACT
The variable spatial scale and crowd distribution in crowd images are the main challenges faced by crowd counting problems in recent years. In order to solve the above problems, a crowd counting method based on a multi-scale adaptive network is proposed in this paper. The first 10 layers of the VGG-16 network are used to extract basic features, and the spatial pyramid pooling layer is introduced to make the network adapt to images of any size. Then, multi-scale features are extracted through hybrid dilated convolution, and contrast features are obtained by comparing with basic features. Finally, the weight and density map are calculated to obtain the number of people according to contrast features. The experimental results on the Shanghai Tech and UCF_CC_50 datasets show that, compared with the previous best method, the MAE of the two parts of the Shanghai Tech dataset in this paper is reduced by 1.1 and 0.1, respectively, the MSE is the same in part A, and the part B is reduced by 0.2. On the UCF_CC_50 dataset, the MAE is reduced by 10.9, and the MSE is reduced by 61.4. It shows that the method proposed in this paper has better accuracy and robustness.
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Index Terms
- Research on Crowd Counting Algorithm Based on Multi-scale Adaptive Network
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