About
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
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Datasets
Greatest papers with code
Densely Connected Convolutional Networks
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.
Ranked #1 on
Pedestrian Attribute Recognition
on UAV-Human
BREAST TUMOUR CLASSIFICATION CROWD COUNTING IMAGE CLASSIFICATION OBJECT RECOGNITION PEDESTRIAN ATTRIBUTE RECOGNITION PERSON RE-IDENTIFICATION
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
• osmr/imgclsmob
• 
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on
Medical Image Segmentation
on RITE
CLASSIFICATION CROWD COUNTING LESION SEGMENTATION REAL-TIME SEMANTIC SEGMENTATION SCENE SEGMENTATION SCENE UNDERSTANDING
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.
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.
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).
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
CVPR 2018
• leeyeehoo/CSRNet-pytorch
•
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.
Ranked #3 on
Crowd Counting
on Venice
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Conference 2016
• svishwa/crowdcount-mcnn
•
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.
Ranked #5 on
Crowd Counting
on Venice
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
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.
ACTIVE LEARNING CROWD COUNTING IMAGE QUALITY ASSESSMENT LEARNING-TO-RANK SELF-SUPERVISED LEARNING
Bayesian Loss for Crowd Count Estimation with Point Supervision
ICCV 2019
• ZhihengCV/Bayesian-Crowd-Counting
•
In crowd counting datasets, each person is annotated by a point, which is usually the center of the head.
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds.