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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 351–371 of 371 papers

TitleStatusHype
Mask-aware networks for crowd counting—0
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting—0
Möbius Transform for Mitigating Perspective Distortions in Representation Learning—0
Modeling Noisy Annotations for Crowd Counting—0
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting—0
Multi-channel Deep Supervision for Crowd Counting—0
Multi-Level Attentive Convoluntional Neural Network for Crowd Counting—0
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting—0
Multimodal Crowd Counting with Pix2Pix GANs—0
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting—0
Multi-scale Feature Aggregation for Crowd Counting—0
Multi-source Multi-scale Counting in Extremely Dense Crowd Images—0
NAS-Count: Counting-by-Density with Neural Architecture Search—0
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images—0
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning—0
PaDNet: Pan-Density Crowd Counting—0
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting—0
Parametric Regression on the Grassmannian—0
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting—0
People Counting in High Density Crowds from Still Images—0
Pixel-wise Crowd Understanding via Synthetic Data—0
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