Crowd Counting
137 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
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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
Focal Inverse Distance Transform Maps for Crowd Localization
dk-liang/FIDTM
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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.
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
TencentYoutuResearch/CrowdCounting-SASNet
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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.
Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
tencentyouturesearch/crowdcounting-p2pnet
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In this paper, we propose a purely point-based framework for joint crowd counting and individual localization.
Inception-Based Crowd Counting -- Being Fast while Remaining Accurate
gjy3035/Awesome-Crowd-Counting
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Recent sophisticated CNN-based algorithms have demonstrated their extraordinary ability to automate counting crowds from images, thanks to their structures which are designed to address the issue of various head scales.
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.
Improving Object Counting with Heatmap Regulation
littleaich/heatmap-regulation
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Adding HR to a simple VGG front-end improves performance on all these benchmarks compared to a simple one-look baseline model and results in state-of-the-art performance for car counting.
Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting
pxq0312/SFANet-crowd-counting
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The task of crowd counting in varying density scenes is an extremely difficult challenge due to large scale variations.
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.
Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection
val-iisc/lsc-cnn
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We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm.
AutoScale: Learning to Scale for Crowd Counting and Localization
A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.
