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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 251–275 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
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