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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 201–250 of 371 papers

TitleStatusHype
Direct Measure Matching for Crowd Counting—0
NAS-Count: Counting-by-Density with Neural Architecture Search—0
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images—0
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning—0
PaDNet: Pan-Density Crowd Counting—0
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting—0
Parametric Regression on the Grassmannian—0
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting—0
People Counting in High Density Crowds from Still Images—0
Pixel-wise Crowd Understanding via Synthetic Data—0
Plug-and-Play Rescaling Based Crowd Counting in Static Images—0
Point in, Box out: Beyond Counting Persons in Crowds—0
POPCat: Propagation of particles for complex annotation tasks—0
ProgRoCC: A Progressive Approach to Rough Crowd Counting—0
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks—0
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method—0
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization—0
Recurrent Distillation based Crowd Counting—0
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting—0
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting—0
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting—0
Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting—0
Relational Attention Network for Crowd Counting—0
Relevant Region Prediction for Crowd Counting—0
Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives—0
Revisiting Perspective Information for Efficient Crowd Counting—0
Robust Regression via Deep Negative Correlation Learning—0
Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM—0
Semi-Supervised Crowd Counting from Unlabeled Data—0
Multi-Scale Attention Network for Crowd Counting—0
Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network—0
Scale-Aware Crowd Count Network with Annotation Error Correction—0
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background—0
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting—0
Scene-Adaptive Attention Network for Crowd Counting—0
Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)—0
Self-supervised Domain Adaptation in Crowd Counting—0
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks—0
Shallow Feature Based Dense Attention Network for Crowd Counting—0
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead—0
SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting—0
Soft-Margin Mixture of Regressions—0
Spatiotemporal Modeling for Crowd Counting in Videos—0
SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing—0
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting—0
Structured Inhomogeneous Density Map Learning for Crowd Counting—0
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization—0
The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network—0
Top-Down Feedback for Crowd Counting Convolutional Neural Network—0
Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models—0
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