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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 226–250 of 371 papers

TitleStatusHype
Pixel-wise Crowd Understanding via Synthetic Data—0
Learning Error-Driven Curriculum for Crowd Counting—0
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks—0
Fine-Grained Crowd Counting—0
Active Crowd Counting with Limited Supervision—0
Dense Crowds Detection and Counting with a Lightweight Architecture—0
Bayesian Multi Scale Neural Network for Crowd Counting—0
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks—0
Learning to Count in the Crowd from Limited Labeled Data—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
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method—0
Understanding the impact of mistakes on background regions in crowd counting—0
3D Crowd Counting via Geometric Attention-guided Multi-View Fusion—0
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning—0
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
Hybrid Graph Neural Networks for Crowd Counting—0
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