Crowd Counting
150 papers with code • 13 benchmarks • 23 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.
Benchmarks
These leaderboards are used to track progress in Crowd Counting
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Crowd Counting models and implementationsDatasets
ShanghaiTech
UCF-QNRF
JHU-CROWD++
UCF-CC-50
NWPU-Crowd
JHU-CROWD
CityStreet
CVCS
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
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.
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.
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.
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
gjy3035/Awesome-Crowd-Counting
•
•
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.
Context-Aware Crowd Counting
weizheliu/Context-Aware-Crowd-Counting
•
•
CVPR 2019
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
C^3 Framework: An Open-source PyTorch Code for Crowd Counting
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).
CNN-based Density Estimation and Crowd Counting: A Survey
gjy3035/Awesome-Crowd-Counting
•
•
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
Focal Inverse Distance Transform Maps for Crowd Localization
dk-liang/FIDTM
•
•
Most regression-based methods utilize convolution neural networks (CNN) to regress a density map, which can not accurately locate the instance in the extremely dense scene, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map.
