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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 151–175 of 371 papers

TitleStatusHype
Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd CountingCode1
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd CountingCode1
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation—0
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting—0
Congested Crowd Instance Localization with Dilated Convolutional Swin TransformerCode1
Cascaded Residual Density Network for Crowd Counting—0
Spatial Uncertainty-Aware Semi-Supervised Crowd CountingCode1
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network—0
Rethinking Counting and Localization in Crowds:A Purely Point-Based FrameworkCode1
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd CountingCode1
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge ResultsCode1
Crowd Counting via Perspective-Guided Fractional-Dilation ConvolutionCode0
Direct Measure Matching for Crowd Counting—0
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting—0
A Generalized Loss Function for Crowd Counting and Localization—0
Hybrid attention network based on progressive embedding scale-context for crowd counting—0
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
Detection, Tracking, and Counting Meets Drones in Crowds: A BenchmarkCode1
Deep learning with self-supervision and uncertainty regularization to count fish in underwater imagesCode1
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting—0
Dense Point Prediction: A Simple Baseline for Crowd Counting and LocalizationCode1
Towards Adversarial Patch Analysis and Certified Defense against Crowd CountingCode0
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