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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 1–50 of 371 papers

TitleStatusHype
Inception-Based Crowd Counting -- Being Fast while Remaining AccurateCode4
Towards Automatic Power Battery Detection: New Challenge Benchmark Dataset and BaselineCode3
Single Domain Generalization for Crowd CountingCode2
Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and BaselineCode2
Improving Point-based Crowd Counting and Localization Based on Auxiliary Point GuidanceCode2
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise ClassificationCode2
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and LocalizationCode2
CNN-based Density Estimation and Crowd Counting: A SurveyCode2
HumanBench: Towards General Human-centric Perception with Projector Assisted PretrainingCode2
Improving Deep Regression with Ordinal EntropyCode1
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-ConquerCode1
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception TasksCode1
The Effectiveness of a Simplified Model Structure for Crowd CountingCode1
Gramformer: Learning Crowd Counting via Graph-Modulated TransformerCode1
Few-Shot Scene Adaptive Crowd Counting Using Meta-LearningCode1
FusionCount: Efficient Crowd Counting via Multiscale Feature FusionCode1
Efficient Crowd Counting via Structured Knowledge TransferCode1
DR.VIC: Decomposition and Reasoning for Video Individual CountingCode1
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural NetworksCode1
EBC-ZIP: Improving Blockwise Crowd Counting with Zero-Inflated Poisson RegressionCode1
Completely Self-Supervised Crowd Counting via Distribution MatchingCode1
Congested Crowd Instance Localization with Dilated Convolutional Swin TransformerCode1
Counting People by Estimating People FlowsCode1
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd CountingCode1
Crowd Counting in the Frequency DomainCode1
Free Lunch Enhancements for Multi-modal Crowd CountingCode1
Adaptive Mixture Regression Network with Local Counting Map for Crowd CountingCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd CountingCode1
Harnessing Perceptual Adversarial Patches for Crowd CountingCode1
Distribution Matching for Crowd CountingCode1
CLIP-Count: Towards Text-Guided Zero-Shot Object CountingCode1
Detection, Tracking, and Counting Meets Drones in Crowds: A BenchmarkCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd CountingCode1
Dense Point Prediction: A Simple Baseline for Crowd Counting and LocalizationCode1
Backdoor Attacks on Crowd CountingCode1
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion ModelsCode1
AdaCrowd: Unlabeled Scene Adaptation for Crowd CountingCode1
CCTrans: Simplifying and Improving Crowd Counting with TransformerCode1
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory SignalCode1
Bi-level Alignment for Cross-Domain Crowd CountingCode1
A Self-Training Approach for Point-Supervised Object Detection and Counting in CrowdsCode1
Boosting Crowd Counting via Multifaceted AttentionCode1
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary TransformerCode1
Boosting Detection in Crowd Analysis via Underutilized Output FeaturesCode1
Deep learning with self-supervision and uncertainty regularization to count fish in underwater imagesCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
Cross-head Supervision for Crowd Counting with Noisy AnnotationsCode1
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme ConditionsCode1
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