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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 101–125 of 371 papers

TitleStatusHype
A Survey on Computer Vision based Human Analysis in the COVID-19 Era—0
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN—0
DroneNet: Crowd Density Estimation using Self-ONNs for Drones—0
Crowd Counting with Density Adaption Networks—0
Crowd Counting with Deep Structured Scale Integration Network—0
CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting—0
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View—0
Crowd Counting with Decomposed Uncertainty—0
Crowd counting with crowd attention convolutional neural network—0
Crowd Counting with Online Knowledge Learning—0
Causal Influence in Federated Edge Inference—0
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps—0
Cascaded Residual Density Network for Crowd Counting—0
Crowd Density Estimation using Imperfect Labels—0
A Study of Human Gaze Behavior During Visual Crowd Counting—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
Adaptive Density Map Generation for Crowd Counting—0
Diffusion-based Data Augmentation for Object Counting Problems—0
A Strong Baseline for Crowd Counting and Unsupervised People Localization—0
Crowd Counting via Hierarchical Scale Recalibration Network—0
Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization—0
Cumulative Attribute Space for Age and Crowd Density Estimation—0
Calibrating Uncertainty for Semi-Supervised Crowd Counting—0
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