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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 201–250 of 371 papers

TitleStatusHype
Uncertainty Estimation and Sample Selection for Crowd CountingCode0
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View—0
Distribution Matching for Crowd CountingCode1
A Study of Human Gaze Behavior During Visual Crowd Counting—0
Completely Self-Supervised Crowd Counting via Distribution MatchingCode1
Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark MethodCode1
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting—0
Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations—0
Pixel-wise Crowd Understanding via Synthetic Data—0
A Self-Training Approach for Point-Supervised Object Detection and Counting in CrowdsCode1
Learning Error-Driven Curriculum for Crowd Counting—0
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks—0
Weighing Counts: Sequential Crowd Counting by Reinforcement LearningCode1
Active Crowd Counting with Limited Supervision—0
Fine-Grained Crowd Counting—0
Dense Crowds Detection and Counting with a Lightweight Architecture—0
Bayesian Multi Scale Neural Network for Crowd Counting—0
Exploit the potential of Multi-column architecture for Crowd CountingCode1
Learning to Count in the Crowd from Limited Labeled Data—0
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks—0
Shallow Feature Based Dense Attention Network for Crowd Counting—0
Recurrent Distillation based Crowd Counting—0
Attention Scaling for Crowd Counting—0
Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting—0
Relevant Region Prediction for Crowd Counting—0
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme ConditionsCode1
Adaptive Mixture Regression Network with Local Counting Map for Crowd CountingCode1
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method—0
Neuron Linear Transformation: Modeling the Domain Shift for Crowd CountingCode1
Understanding the impact of mistakes on background regions in crowd counting—0
CNN-based Density Estimation and Crowd Counting: A SurveyCode2
Efficient Crowd Counting via Structured Knowledge TransferCode1
3D Crowd Counting via Geometric Attention-guided Multi-View Fusion—0
Encoder-Decoder Based Convolutional Neural Network with Multi-Scale-Aware Modules for Crowd CountingCode1
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning—0
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd CountingCode1
Crowd Counting via Hierarchical Scale Recalibration Network—0
NAS-Count: Counting-by-Density with Neural Architecture Search—0
Towards Using Count-level Weak Supervision for Crowd Counting—0
ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images—0
Multi-Stream Networks and Ground-Truth Generation for Crowd CountingCode0
A Real-Time Deep Network for Crowd CountingCode0
Few-Shot Scene Adaptive Crowd Counting Using Meta-LearningCode1
Hybrid Graph Neural Networks for Crowd Counting—0
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting—0
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and LocalizationCode2
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
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