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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 251–300 of 371 papers

TitleStatusHype
Towards a Universal Model for Cross-Dataset Crowd Counting—0
Towards Building a Real Time Mobile Device Bird Counting System Through Synthetic Data Training and Model Compression—0
Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network—0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
Towards Using Count-level Weak Supervision for Crowd Counting—0
Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things—0
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection—0
Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning—0
Understanding the impact of mistakes on background regions in crowd counting—0
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting—0
Vicinal Counting Networks—0
Fast Video Crowd Counting with a Temporal Aware Network—0
Video Individual Counting for Moving Drones—0
VMambaCC: A Visual State Space Model for Crowd Counting—0
Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations—0
Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications—0
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs—0
W-Net: Reinforced U-Net for Density Map Estimation—0
3D Crowd Counting via Geometric Attention-guided Multi-View Fusion—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
Mask-aware networks for crowd counting—0
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting—0
Möbius Transform for Mitigating Perspective Distortions in Representation Learning—0
Modeling Noisy Annotations for Crowd Counting—0
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting—0
Multi-channel Deep Supervision for Crowd Counting—0
Multi-Level Attentive Convoluntional Neural Network for Crowd Counting—0
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting—0
Multimodal Crowd Counting with Pix2Pix GANs—0
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