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# Crowd Counting Edit

27 papers with code · Computer Vision

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting

10 Jan 2020gjy3035/Awesome-Crowd-Counting

In the last decade, crowd counting attracts much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc.

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# C^3 Framework: An Open-source PyTorch Code for Crowd Counting

5 Jul 2019gjy3035/C-3-Framework

This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F).

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# Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

17 Feb 2019xialeiliu/RankIQA

Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.

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# CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting

Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations.

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# Switching Convolutional Neural Network for Crowd Counting

It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.

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# Crowd counting via scale-adaptive convolutional neural network

13 Nov 2017miao0913/SaCNN-CrowdCounting-Tencent_Youtu

The task of crowd counting is to automatically estimate the pedestrian number in crowd images.

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# Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection

18 Jun 2019val-iisc/lsc-cnn

We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm.

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# Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework.

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# Bayesian Loss for Crowd Count Estimation with Point Supervision

In crowd counting datasets, each person is annotated by a point, which is usually the center of the head.

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# From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

A dense region can always be divided until sub-region counts are within the previously observed closed set.

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