Perspective-Aware CNN For Crowd Counting

Abstract : Crowd counting is the task of estimating pedestrian numbers in crowd images. Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions. A major challenge of this task lies in the drastic changes of scales and perspectives in images. Representative approaches usually utilize different (large) sized filters and conduct patch-based estimations to tackle it, which is however computationally expensive. In this paper, we propose a perspective-aware convolutional neural network (PACNN) with a single backbone of small filters (e.g. 3 x 3). It directly predicts a perspective map in the network and encodes it as a perspective-aware weighting layer to adaptively combine the density outputs from multi-scale feature maps. The weights are learned at every pixel of the map such that the final combination is robust to perspective changes and pedestrian size variations. We conduct extensive experiments on the ShanghaiTech, WorldExpo'10 and UCF_CC_50 datasets, and demonstrate that PACNN achieves state-of-the-art results and runs as fast as the fastest.
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[Research Report] Inria Rennes - Bretagne Atlantique. 2018
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Contributeur : Miaojing Shi <>
Soumis le : jeudi 5 juillet 2018 - 15:50:46
Dernière modification le : samedi 7 juillet 2018 - 01:18:06

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  • HAL Id : hal-01831109, version 1

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Miaojing Shi, Zhaohui Yang, Chao Xu, Qijun Chen. Perspective-Aware CNN For Crowd Counting. [Research Report] Inria Rennes - Bretagne Atlantique. 2018. 〈hal-01831109〉

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