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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 251–300 of 371 papers

TitleStatusHype
CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting—0
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction—0
Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance—0
Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention NetworkCode1
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting—0
Crowd Counting via Segmentation Guided Attention Networks and Curriculum LossCode0
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method—0
Relational Attention Network for Crowd Counting—0
Attentional Neural Fields for Crowd Counting—0
Adaptive Density Map Generation for Crowd Counting—0
MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground ImageryCode0
In-field grape berries counting for yield estimation using dilated CNNs—0
Count-guided Weakly Supervised Localization Based on Density Map—0
Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things—0
Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting—0
Learning Spatial Awareness to Improve Crowd Counting—0
Perspective-Guided Convolution Networks for Crowd CountingCode0
Crowd Counting on Images with Scale Variation and Isolated ClustersCode0
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting—0
Robust Regression via Deep Negative Correlation Learning—0
Crowd Counting with Deep Structured Scale Integration Network—0
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-ConquerCode1
Enhanced 3D convolutional networks for crowd counting—0
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
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting—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
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
CODA: Counting Objects via Scale-aware Adversarial Density AdaptionCode0
Crowd Counting with Decomposed Uncertainty—0
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