Browse > Computer Vision > Crowds > Crowd Counting

# Crowd Counting Edit

21 papers with code · Computer Vision

Trend Dataset Best Method Paper title Paper Code Compare

# Bayesian Loss for Crowd Count Estimation with Point Supervision

10 Aug 2019ZhihengCV/Bayesian-Crowd-Counting

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

22
10 Aug 2019

# Deep Density-aware Count Regressor

9 Aug 2019GeorgeChenZJ/deepcount

We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency.

8
09 Aug 2019

# Locality-constrained Spatial Transformer Network for Video Crowd Counting

18 Jul 2019sweetyy83/Lstn_fdst_dataset

Then to relate the density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the density map of next frame with that of current frame.

4
18 Jul 2019

# 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).

235
05 Jul 2019

# 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.

70
18 Jun 2019

# PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

24 May 2019gjy3035/PCC-Net

Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion.

33
24 May 2019

25 Mar 2019Willy0919/CODA

Extensive experiments demonstrate that our network produces much better results on unseen datasets compared with existing counting adaption models.

14
25 Mar 2019

# 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.

193
17 Feb 2019

# Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting

4 Feb 2019pxq0312/SFANet-crowd-counting

The task of crowd counting in varying density scenes is an extremely difficult challenge due to large scale variations.

5
04 Feb 2019

# Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance

22 Aug 2018siyuhuang/crowdcount-stackpool

In this work, we explore the cross-scale similarity in crowd counting scenario, in which the regions of different scales often exhibit high visual similarity.

73
22 Aug 2018