Crowd Counting

69 papers with code • 6 benchmarks • 14 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

Greatest papers with code

Densely Connected Convolutional Networks

pytorch/vision 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.

Breast Tumour Classification Crowd Counting +4

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

osmr/imgclsmob 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.

Crowd Counting General Classification +4

CNN-based Density Estimation and Crowd Counting: A Survey

gjy3035/Awesome-Crowd-Counting 28 Mar 2020

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.

Crowd Counting Density Estimation +1

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.

Crowd Counting

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).

Crowd Counting

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.

Crowd Counting Scene Recognition

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.

Crowd Counting

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

xialeiliu/RankIQA 17 Feb 2019

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 +3

Bayesian Loss for Crowd Count Estimation with Point Supervision

ZhihengCV/Bayesian-Crowd-Counting ICCV 2019

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

Crowd Counting

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

davideverona/deep-crowd-counting_crowdnet 22 Aug 2016

Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds.

Crowd Counting Data Augmentation