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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 76–100 of 371 papers

TitleStatusHype
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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