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Multi-scale Convolutional Neural Networks for Crowd Counting
Crowd counting on static images is a challenging problem due to scale va...
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Iterative Crowd Counting
In this work, we tackle the problem of crowd counting in images. We pres...
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Segmentation Guided Attention Network for Crowd Counting via Curriculum Learning
Crowd counting using deep convolutional neural networks (CNN) has achiev...
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Perspective-Aware CNN For Crowd Counting
Crowd counting is the task of estimating pedestrian numbers in crowd ima...
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Pyramid Scale Network for Crowd Counting
Crowd counting is a challenging task in computer vision due to serious o...
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Dense Scale Network for Crowd Counting
Crowd counting has been widely studied by computer vision community in r...
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Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
In this work, we propose a novel crowd counting network that progressive...
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Perspective-Guided Convolution Networks for Crowd Counting
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. PGCNet, on the other hand, utilizes perspective information to guide the spatially variant smoothing of feature maps before feeding them to the successive convolutions. An effective perspective estimation branch is also introduced to PGCNet, which can be trained in either supervised setting or weakly-supervised setting when the branch has been pre-trained. Our PGCNet is single-column with moderate increase in computation, and extensive experimental results on four benchmark datasets show the improvements of our method against the state-of-the-arts. Additionally, we also introduce Crowd Surveillance, a large scale dataset for crowd counting that contains 13,000+ high-resolution images with challenging scenarios.
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