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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 51–100 of 371 papers

TitleStatusHype
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting—0
Learning Discriminative Features for Crowd Counting—0
Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian KernelCode0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd ScenesCode1
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs—0
SYRAC: Synthesize, Rank, and CountCode0
Boosting Detection in Crowd Analysis via Underutilized Output FeaturesCode1
Point-Query Quadtree for Crowd Counting, Localization, and MoreCode1
Calibrating Uncertainty for Semi-Supervised Crowd Counting—0
DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd CountingCode1
Improved Knowledge Distillation for Crowd Counting on IoT DeviceCode0
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary TransformerCode1
Counting Crowds in Bad Weather—0
Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement—0
CLIP-Count: Towards Text-Guided Zero-Shot Object CountingCode1
Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications—0
Crowd Counting with Sparse Annotation—0
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning—0
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception TasksCode1
Application-Driven AI Paradigm for Person Counting in Various Scenarios—0
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion ModelsCode1
Crowd Counting with Online Knowledge Learning—0
Cross-head Supervision for Crowd Counting with Noisy AnnotationsCode1
Super-Resolution Information Enhancement For Crowd CountingCode1
HumanBench: Towards General Human-centric Perception with Projector Assisted PretrainingCode2
LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance—0
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural NetworksCode1
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks—0
Improving Deep Regression with Ordinal EntropyCode1
Density-based clustering with fully-convolutional networks for crowd flow detection from dronesCode0
RGB-T Multi-Modal Crowd Counting Based on TransformerCode1
Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised CountingCode1
A Unified Object Counting Network with Object Occupation PriorCode0
Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networksCode1
HDNet: A Hierarchically Decoupled Network for Crowd Counting—0
Progressive Multi-resolution Loss for Crowd CountingCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
Crowd Density Estimation using Imperfect Labels—0
Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects—0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning—0
DroneNet: Crowd Density Estimation using Self-ONNs for Drones—0
Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network—0
A Survey on Computer Vision based Human Analysis in the COVID-19 Era—0
Spatio-channel Attention Blocks for Cross-modal Crowd CountingCode1
Inception-Based Crowd Counting -- Being Fast while Remaining AccurateCode4
Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd CountingCode0
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection—0
Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives—0
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