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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 226–250 of 371 papers

TitleStatusHype
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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