SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 371 papers

TitleStatusHype
Towards Using Count-level Weak Supervision for Crowd Counting—0
Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things—0
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection—0
Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning—0
Understanding the impact of mistakes on background regions in crowd counting—0
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting—0
Vicinal Counting Networks—0
Fast Video Crowd Counting with a Temporal Aware Network—0
Video Individual Counting for Moving Drones—0
VMambaCC: A Visual State Space Model for Crowd Counting—0
Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations—0
Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications—0
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs—0
W-Net: Reinforced U-Net for Density Map Estimation—0
3D Crowd Counting via Geometric Attention-guided Multi-View Fusion—0
ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images—0
A-CCNN: adaptive ccnn for density estimation and crowd counting—0
Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement—0
Active Crowd Counting with Limited Supervision—0
Adaptive Density Map Generation for Crowd Counting—0
A Deeply-Recursive Convolutional Network for Crowd Counting—0
A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video—0
Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining—0
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View—0
A Generalized Loss Function for Crowd Counting and Localization—0
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
Show:102550
← PrevPage 5 of 8Next →

No leaderboard results yet.