Perspective-Guided Convolution Networks for Crowd Counting

ICCV 2019 Zhaoyi YanYuchen YuanWangmeng ZuoXiao TanYezhen WangShilei WenErrui Ding

In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e. PGCNet), which aims to overcome the dramatic intra-scene scale variations of people due to the perspective effect. While most state-of-the-arts adopt multi-scale or multi-column architectures to address such issue, they generally fail in modeling continuous scale variations since only discrete representative scales are considered... (read more)

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