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Crowd Counting

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

Papers

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Showing 101–150 of 371 papers

TitleStatusHype
Uncertainty Estimation and Sample Selection for Crowd CountingCode0
Scale Aggregation Network for Accurate and Efficient Crowd CountingCode0
Crowd Counting With Deep Negative Correlation LearningCode0
Crowd Counting via Segmentation Guided Attention Networks and Curriculum LossCode0
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level ClassificationCode0
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd CountingCode0
Crowd counting via scale-adaptive convolutional neural networkCode0
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density EstimationCode0
Residual Regression With Semantic Prior for Crowd CountingCode0
Crowd Counting via Perspective-Guided Fractional-Dilation ConvolutionCode0
Car Object Counting and Position Estimation via Extension of the CLIP-EBC FrameworkCode0
Crowd Counting via Adversarial Cross-Scale Consistency PursuitCode0
Perspective-Guided Convolution Networks for Crowd CountingCode0
C^3 Framework: An Open-source PyTorch Code for Crowd CountingCode0
Crowd Counting on Images with Scale Variation and Isolated ClustersCode0
PCC Net: Perspective Crowd Counting via Spatial Convolutional NetworkCode0
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd CountingCode0
Point-to-Region Loss for Semi-Supervised Point-Based Crowd CountingCode0
Semi-supervised Counting via Pixel-by-pixel Density Distribution ModellingCode0
CrowdNet: A Deep Convolutional Network for Dense Crowd CountingCode0
Multi-Scale Context Aggregation Network with Attention-Guided for Crowd CountingCode0
Multi-modal Crowd Counting via Modal EmulationCode0
Multi-scale Convolutional Neural Networks for Crowd CountingCode0
Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd CountingCode0
Multi-Stream Networks and Ground-Truth Generation for Crowd CountingCode0
MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground ImageryCode0
Locate, Size and Count: Accurately Resolving People in Dense Crowds via DetectionCode0
A Real-Time Deep Network for Crowd CountingCode0
Leveraging Unlabeled Data for Crowd Counting by Learning to RankCode0
Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd CountingCode0
Improving Object Counting with Heatmap RegulationCode0
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale UpsamplingCode0
Locality-constrained Spatial Transformer Network for Video Crowd CountingCode0
Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian KernelCode0
Bayesian Loss for Crowd Count Estimation with Point SupervisionCode0
ANTHROPOS-V: benchmarking the novel task of Crowd Volume EstimationCode0
Image Crowd Counting Using Convolutional Neural Network and Markov Random FieldCode0
Discrete-Constrained Regression for Local Counting ModelsCode0
AutoScale: Learning to Scale for Crowd Counting and LocalizationCode0
ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd UnderstandingCode0
An Improved Normed-Deformable Convolution for Crowd CountingCode0
Improved Knowledge Distillation for Crowd Counting on IoT DeviceCode0
CountFormer: Multi-View Crowd Counting TransformerCode0
Density-based clustering with fully-convolutional networks for crowd flow detection from dronesCode0
A Unified Object Counting Network with Object Occupation PriorCode0
DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance SynergyCode0
Dense Scale Network for Crowd CountingCode0
Context-Aware Crowd CountingCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City ApplicationsCode0
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