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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 276–300 of 371 papers

TitleStatusHype
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps—0
Crowd counting with crowd attention convolutional neural network—0
Crowd Counting with Decomposed Uncertainty—0
Crowd Counting with Deep Structured Scale Integration Network—0
Crowd Counting with Density Adaption Networks—0
Crowd Counting with Online Knowledge Learning—0
Crowd counting with segmentation attention convolutional neural network—0
Crowd Counting with Sparse Annotation—0
Crowd Density Estimation using Imperfect Labels—0
Crowd Density Estimation using Novel Feature Descriptor—0
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models—0
CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability—0
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP—0
Crowd Scene Analysis using Deep Learning Techniques—0
Crowd Transformer Network—0
Cumulative Attribute Space for Age and Crowd Density Estimation—0
Curriculum for Crowd Counting -- Is it Worthy?—0
DAFE-FD: Density Aware Feature Enrichment for Face Detection—0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning—0
DeepCount: Crowd Counting with WiFi via Deep Learning—0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks—0
Deep Spatial Regression Model for Image Crowd Counting—0
Dense Crowds Detection and Counting with a Lightweight Architecture—0
Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization—0
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