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CAFN: The Combination of Atrous and Fractionally Strided Convolutional Neural Networks for Understanding the Densely Crowded Scenes

  • Lvyuan Fan
  • Minglei TongEmail author
  • Min Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

The task to estimate crowd count in highly clustered scenes is extremely challenged on account of variable scales with non-uniformity. This paper aims to develop a simple but valid method that concentrates on predicting the density map accurately. We proposed a combination of atrous and fractionally strided convolutional neural network (CAFN), which is merely constituted by two components: an atrous convolutional neural network as the front-end for 2D features extraction which utilizes dilated kernels to deliver larger receptive fields and to lessen the network parameters, a fractionally strided convolutional neural network for the back-end to lower the loss of details during down-sampling. CAFN is an easy-trained model because of its unadulterated convolutional structure. We demonstrated CAFN on three datasets (Shanghai Tech dataset A and B, UCF_CC_50) and deliver satisfactory performance. Additionally, CAFN achieves lower Mean Absolute Error (MAE) on Shanghai Tech A (MAE = 100.8), UCF_CC_50 (MAE = 305.3) while the experiment results reveal that the proposed model can effectively lower estimation errors when compared with previous methods.

Keywords

CAFN Crowd density estimation Atrous convolutions Fractionally strided convolutions 

Notes

Acknowledgement

Sponsored by Natural Science Foundation of Shanghai (16ZR1413300).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Shanghai University of Electric PowerShanghaiPeople’s Republic of China

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