1. 静态图像中采用混合卷积结构进行人群密度估计.
- Author
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范绿源, 仝明磊, 李 敏, and 南 昊
- Subjects
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CONVOLUTIONAL neural networks , *TEST methods , *ALGORITHMS , *DENSITY , *CROWDS - Abstract
This paper developed a hybrid convolution neural network for perceptual crowd counting, which could accurately predict density maps in extremely crowded scenes . It consisted of merely two components: the front-end was a dilated convolutional neural network to extract two-dimensional features ;the back-end deployed a fractionally stride convolution to lower the loss of image information caused by down-sampling. This paper designed the model structure based on the dataset Shanghai Tech, then in an attempt to acknowledge and analyze the performance of the algorithm, and afterwards made use of the evaluation indicators of the regression problem, the average absolute error ( MAE) and the mean-square error ( MSE) as the criteria. Additionally, testing the method on Shanghai Tech ( MAE= 100. 8), UCF CC_50 ( MAE = 305. 3) and WorldExpo' 10 datasets while the experiment results reveal that the proposed model can effectively reduce MAE and MSE when compared with previous methods . [ABSTRACT FROM AUTHOR]
- Published
- 2020
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