Crowd Counting

90 papers with code • 7 benchmarks • 17 datasets

Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.

Source: Deep Density-aware Count Regressor

Libraries

Use these libraries to find Crowd Counting models and implementations

Most implemented papers

Densely Connected Convolutional Networks

liuzhuang13/DenseNet CVPR 2017

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

PaddlePaddle/PaddleSeg 2 Nov 2015

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

leeyeehoo/CSRNet-pytorch CVPR 2018

We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.

Bayesian Loss for Crowd Count Estimation with Point Supervision

ZhihengCV/Baysian-Crowd-Counting ICCV 2019

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

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

xhp-hust-2018-2011/S-DCNet ICCV 2019

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

NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

gjy3035/Awesome-Crowd-Counting 10 Jan 2020

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

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

svishwa/crowdcount-mcnn Conference 2016

To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map.

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

gjy3035/C-3-Framework 5 Jul 2019

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).

Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd

dk-liang/FIDTM 16 Feb 2021

In this paper, we propose a novel map for dense crowd localization and crowd counting.

Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting

TencentYoutuResearch/CrowdCounting-SASNet 27 Jul 2021

Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk.