Crowd Counting
138 papers with code • 10 benchmarks • 19 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
Most implemented papers
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting
•
•
Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN).
Efficient Crowd Counting via Structured Knowledge Transfer
HCPLab-SYSU/SKT
•
•
Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications.
Exploit the potential of Multi-column architecture for Crowd Counting
JunhaoCheng/Pyramid_Scale_Network
•
•
To the best of our knowledge, PSNet is the first work to explicitly address scale limitation and feature similarity in multi-column design.
A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds
WangyiNTU/Point-supervised-crowd-detection
•
•
In this paper, we propose a novel self-training approach named Crowd-SDNet that enables a typical object detector trained only with point-level annotations (i. e., objects are labeled with points) to estimate both the center points and sizes of crowded objects.
CCTrans: Simplifying and Improving Crowd Counting with Transformer
However, the transformer can model the global context easily.
Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting
bridgeqiqi/dream
•
•
Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images.
DR.VIC: Decomposition and Reasoning for Video Individual Counting
taohan10200/drnet
•
•
CVPR 2022
Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame.
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
dk-liang/crowdclip
•
•
CVPR 2023
To the best of our knowledge, CrowdCLIP is the first to investigate the vision language knowledge to solve the counting problem.
Multi-scale Convolutional Neural Networks for Crowd Counting
Ling-Bao/mscnn
•
•
Crowd counting on static images is a challenging problem due to scale variations.
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
lcylmhlcy/ResnetCrowd-Caffe
•
•
In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification.
