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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
Multi-Level Attentive Convoluntional Neural Network for Crowd Counting—0
Rethinking Global Context in Crowd Counting—0
Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification—0
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead—0
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting—0
Towards Adversarial Patch Analysis and Certified Defense against Crowd CountingCode0
Multi-Scale Context Aggregation Network with Attention-Guided for Crowd CountingCode0
Multi-channel Deep Supervision for Crowd Counting—0
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization—0
Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd EstimationCode0
Enhanced Information Fusion Network for Crowd Counting—0
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background—0
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network—0
Crowd Counting With Partial Annotations in an ImageCode0
Towards a Universal Model for Cross-Dataset Crowd Counting—0
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting—0
Modeling Noisy Annotations for Crowd Counting—0
A Strong Baseline for Crowd Counting and Unsupervised People Localization—0
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting—0
Uncertainty Estimation and Sample Selection for Crowd CountingCode0
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View—0
A Study of Human Gaze Behavior During Visual Crowd Counting—0
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
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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