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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 51–75 of 371 papers

TitleStatusHype
Domain-General Crowd Counting in Unseen ScenariosCode1
Distribution Matching for Crowd CountingCode1
Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention NetworkCode1
CCTrans: Simplifying and Improving Crowd Counting with TransformerCode1
Adaptive Mixture Regression Network with Local Counting Map for Crowd CountingCode1
CLIP-Count: Towards Text-Guided Zero-Shot Object CountingCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
Rethinking Spatial Invariance of Convolutional Networks for Object CountingCode1
The Effectiveness of a Simplified Model Structure for Crowd CountingCode1
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory SignalCode1
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd CountingCode1
Encoder-Decoder Based Convolutional Neural Network with Multi-Scale-Aware Modules for Crowd CountingCode1
Congested Crowd Instance Localization with Dilated Convolutional Swin TransformerCode1
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary TransformerCode1
Crowd Counting in the Frequency DomainCode1
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme ConditionsCode1
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd CountingCode1
Cross-head Supervision for Crowd Counting with Noisy AnnotationsCode1
Free Lunch Enhancements for Multi-modal Crowd CountingCode1
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-ConquerCode1
Harnessing Perceptual Adversarial Patches for Crowd CountingCode1
Completely Self-Supervised Crowd Counting via Distribution MatchingCode1
Counting People by Estimating People FlowsCode1
Dense Point Prediction: A Simple Baseline for Crowd Counting and LocalizationCode1
Gramformer: Learning Crowd Counting via Graph-Modulated TransformerCode1
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