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Average up-sample network for crowd counting
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DOI：10.1007/s10489-021-02470-8.png)
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Abstract
En 中文
The task of crowd counting is receiving increased attention recently, but it still faces many challenges, such as extremely dense scene, scale variation and background clutter. The quality of generated density map plays an important role in counting performance. In this paper, we propose an encoder-decoder structure network called Average Up-sample Convolution Neural Network (AU-CNN), for high-quality density map and accurate counting estimation. The encoder extracts the features of input image while the decoder gradually recovers the size of feature map to the original size of input image by developing a simple but effective average up-sample module. The average up-sample module takes the average of interpolation results from three different up-sample methods, without adding any other redundant parameters. Moreover, compared with most existing counting algorithm using only Euclidean loss, we use a combined loss function of Euclidean loss and count loss to optimize the network, which is demonstrated effective in performance improving. Experiments on the ShanghaiTech, UCF_CC_50, and UCF_QNRF demonstrate the great counting performance and robustness of our proposed method.
Keywords:
Crowd counting
Density estimation
Convolutional neural network
Average up-sample
Count loss
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