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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 351–371 of 371 papers

TitleStatusHype
Spatiotemporal Modeling for Crowd Counting in Videos—0
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density EstimationCode0
Soft-Margin Mixture of Regressions—0
Image Crowd Counting Using Convolutional Neural Network and Markov Random FieldCode0
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level ClassificationCode0
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting—0
Multi-scale Convolutional Neural Networks for Crowd CountingCode0
Fully Convolutional Crowd Counting On Highly Congested Scenes—0
Crowd Counting by Adapting Convolutional Neural Networks with Side Information—0
CrowdNet: A Deep Convolutional Network for Dense Crowd CountingCode0
Crowd Counting Considering Network Flow Constraints in Videos—0
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps—0
Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)—0
Single-Image Crowd Counting via Multi-Column Convolutional Neural NetworkCode0
Bayesian Model Adaptation for Crowd Counts—0
People Counting in High Density Crowds from Still Images—0
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks—0
Parametric Regression on the Grassmannian—0
Cumulative Attribute Space for Age and Crowd Density Estimation—0
Crossing the Line: Crowd Counting by Integer Programming with Local Features—0
Multi-source Multi-scale Counting in Extremely Dense Crowd Images—0
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