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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 151–200 of 371 papers

TitleStatusHype
Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd CountingCode1
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd CountingCode1
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation—0
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting—0
Congested Crowd Instance Localization with Dilated Convolutional Swin TransformerCode1
Cascaded Residual Density Network for Crowd Counting—0
Spatial Uncertainty-Aware Semi-Supervised Crowd CountingCode1
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network—0
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd CountingCode1
Rethinking Counting and Localization in Crowds:A Purely Point-Based FrameworkCode1
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge ResultsCode1
Crowd Counting via Perspective-Guided Fractional-Dilation ConvolutionCode0
Direct Measure Matching for Crowd Counting—0
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting—0
A Generalized Loss Function for Crowd Counting and Localization—0
Hybrid attention network based on progressive embedding scale-context for crowd counting—0
Multi-Level Attentive Convoluntional Neural Network for Crowd Counting—0
Rethinking Global Context in Crowd Counting—0
Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification—0
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead—0
Detection, Tracking, and Counting Meets Drones in Crowds: A BenchmarkCode1
Deep learning with self-supervision and uncertainty regularization to count fish in underwater imagesCode1
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting—0
Dense Point Prediction: A Simple Baseline for Crowd Counting and LocalizationCode1
Towards Adversarial Patch Analysis and Certified Defense against Crowd CountingCode0
TransCrowd: weakly-supervised crowd counting with transformersCode1
Multi-Scale Context Aggregation Network with Attention-Guided for Crowd CountingCode0
Leveraging Self-Supervision for Cross-Domain Crowd CountingCode1
Multi-channel Deep Supervision for Crowd Counting—0
Focal Inverse Distance Transform Maps for Crowd LocalizationCode1
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization—0
Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd EstimationCode0
Enhanced Information Fusion Network for Crowd Counting—0
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background—0
Uniformity in Heterogeneity: Diving Deep Into Count Interval Partition for Crowd CountingCode1
Towards a Universal Model for Cross-Dataset Crowd Counting—0
Crowd Counting With Partial Annotations in an ImageCode0
Rethinking Counting and Localization in Crowds: A Purely Point-Based FrameworkCode1
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network—0
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory SignalCode1
Localization in the Crowd with Topological ConstraintsCode1
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting—0
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd CountingCode1
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing ImagesCode1
Wide-Area Crowd Counting: Multi-View Fusion Networks for Counting in Large ScenesCode1
Counting People by Estimating People FlowsCode1
Modeling Noisy Annotations for Crowd Counting—0
A Strong Baseline for Crowd Counting and Unsupervised People Localization—0
AdaCrowd: Unlabeled Scene Adaptation for Crowd CountingCode1
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting—0
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