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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 251–275 of 371 papers

TitleStatusHype
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting—0
Plug-and-Play Rescaling Based Crowd Counting in Static Images—0
AutoScale: Learning to Scale for Crowd Counting and LocalizationCode0
Towards Building a Real Time Mobile Device Bird Counting System Through Synthetic Data Training and Model Compression—0
CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting—0
Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance—0
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction—0
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
Attentional Neural Fields for Crowd Counting—0
Adaptive Density Map Generation for Crowd Counting—0
Relational Attention Network 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
Perspective-Guided Convolution Networks for Crowd CountingCode0
Learning Spatial Awareness to Improve Crowd Counting—0
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
Enhanced 3D convolutional networks for crowd counting—0
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