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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
A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting—0
Application-Driven AI Paradigm for Person Counting in Various Scenarios—0
A Short Overview of Multi-Modal Wi-Fi Sensing—0
A Spatio-Temporal Attentive Network for Video-Based Crowd Counting—0
A Strong Baseline for Crowd Counting and Unsupervised People Localization—0
A Study of Human Gaze Behavior During Visual Crowd Counting—0
A Survey on Computer Vision based Human Analysis in the COVID-19 Era—0
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs—0
Attentional Neural Fields for Crowd Counting—0
Attention Scaling for Crowd Counting—0
Attention to Head Locations for Crowd Counting—0
Audio-Visual Transformer Based Crowd Counting—0
A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting—0
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis—0
Bayesian Model Adaptation for Crowd Counts—0
Bayesian Multi Scale Neural Network for Crowd Counting—0
BBA-net: A bi-branch attention network for crowd counting—0
Boosting Adverse Weather Crowd Counting via Multi-queue Contrastive Learning—0
Rethinking Global Context in Crowd Counting—0
Bound Tightening Network for Robust Crowd Counting—0
Calibrating Uncertainty for Semi-Supervised Crowd Counting—0
Cascaded Residual Density Network for Crowd Counting—0
Causal Influence in Federated Edge Inference—0
CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting—0
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network—0
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