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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
Bayesian Loss for Crowd Count Estimation with Point SupervisionCode0
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting—0
Deep Density-aware Count RegressorCode0
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs—0
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting—0
HA-CCN: Hierarchical Attention-based Crowd Counting Network—0
Locality-constrained Spatial Transformer Network for Video Crowd CountingCode0
C^3 Framework: An Open-source PyTorch Code for Crowd CountingCode0
Fast Video Crowd Counting with a Temporal Aware Network—0
Inverse Attention Guided Deep Crowd Counting Network—0
Dense Scale Network for Crowd CountingCode0
Locate, Size and Count: Accurately Resolving People in Dense Crowds via DetectionCode0
Content-aware Density Map for Crowd Counting and Density Estimation—0
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization—0
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks—0
Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization—0
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting—0
Residual Regression With Semantic Prior for Crowd CountingCode0
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs—0
Counting and Segmenting Sorghum Heads—0
PCC Net: Perspective Crowd Counting via Spatial Convolutional NetworkCode0
Crowd Density Estimation using Novel Feature Descriptor—0
Crowd Transformer Network—0
Point in, Box out: Beyond Counting Persons in Crowds—0
W-Net: Reinforced U-Net for Density Map Estimation—0
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