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. 3x3). 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.

本研究提出了一种透视感知卷积神经网络（PACNN），以解决拥挤图像中的人群计数问题，该网络将透视信息集成到密度回归中，通过生成地面真值透视图并构建透视感知的多尺度密度加权层进行自适应性组合，具有较高的效率和有效性。

重新审视透视信息以提高高效人群计数