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 301–350 of 371 papers

TitleStatusHype
Depth Information Guided Crowd Counting for Complex Crowd Scenes—0
Diffusion-based Data Augmentation for Object Counting Problems—0
Direct Measure Matching for Crowd Counting—0
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN—0
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction—0
DroneNet: Crowd Density Estimation using Self-ONNs for Drones—0
EHNet: An Efficient Hybrid Network for Crowd Counting and Localization—0
Embodied Crowd Counting—0
Enhanced 3D convolutional networks for crowd counting—0
Enhanced Information Fusion Network for Crowd Counting—0
Enhancing and Dissecting Crowd Counting By Synthetic Data—0
Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images—0
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network—0
Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance—0
FGA: Fourier-Guided Attention Network for Crowd Count Estimation—0
FGENet: Fine-Grained Extraction Network for Congested Crowd Counting—0
Fine-Grained Counting with Crowd-Sourced Supervision—0
Fine-Grained Crowd Counting—0
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation—0
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting—0
Fully Convolutional Crowd Counting On Highly Congested Scenes—0
Generalizing semi-supervised generative adversarial networks to regression using feature contrasting—0
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs—0
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting—0
HA-CCN: Hierarchical Attention-based Crowd Counting Network—0
HDNet: A Hierarchically Decoupled Network for Crowd Counting—0
Hybrid attention network based on progressive embedding scale-context for crowd counting—0
Hybrid Graph Neural Networks for Crowd Counting—0
Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting—0
Incorporating Side Information by Adaptive Convolution—0
In Defense of Single-column Networks for Crowd Counting—0
Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes—0
In-field grape berries counting for yield estimation using dilated CNNs—0
Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting—0
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines—0
Inverse Attention Guided Deep Crowd Counting Network—0
Iterative Crowd Counting—0
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method—0
Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting—0
L2HCount:Generalizing Crowd Counting from Low to High Crowd Density via Density Simulation—0
LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance—0
Learning a perspective-embedded deconvolution network for crowd counting—0
Learning Discriminative Features for Crowd Counting—0
Learning Error-Driven Curriculum for Crowd Counting—0
Learning from Synthetic Data for Crowd Counting in the Wild—0
Learning Spatial Awareness to Improve Crowd Counting—0
Learning to Count in the Crowd from Limited Labeled Data—0
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting—0
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting—0
MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting—0
Show:102550
← PrevPage 7 of 8Next →

No leaderboard results yet.