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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 151–200 of 371 papers

TitleStatusHype
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
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
A Spatio-Temporal Attentive Network for Video-Based Crowd Counting—0
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training—0
MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting—0
Multi-scale Feature Aggregation for Crowd Counting—0
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City ApplicationsCode0
Discrete-Constrained Regression for Local Counting ModelsCode0
The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network—0
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation—0
An Improved Normed-Deformable Convolution for Crowd CountingCode0
Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes—0
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd CountingCode0
Self-supervised Domain Adaptation in Crowd Counting—0
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting—0
Fine-Grained Counting with Crowd-Sourced Supervision—0
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting—0
Cross-View Cross-Scene Multi-View Crowd Counting—0
Crowd counting with crowd attention convolutional neural network—0
Crowd counting with segmentation attention convolutional neural network—0
SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing—0
Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches—0
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP—0
Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting—0
CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability—0
Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting—0
TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd CountingCode0
A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting—0
BBA-net: A bi-branch attention network for crowd counting—0
Enhancing and Dissecting Crowd Counting By Synthetic Data—0
Scene-Adaptive Attention Network for Crowd Counting—0
Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network—0
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting—0
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines—0
Vicinal Counting Networks—0
Audio-Visual Transformer Based Crowd Counting—0
Semi-Supervised Crowd Counting from Unlabeled Data—0
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation—0
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting—0
Cascaded Residual Density Network for Crowd Counting—0
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network—0
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
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