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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
Improving Point-based Crowd Counting and Localization Based on Auxiliary Point GuidanceCode2
Single Domain Generalization for Crowd CountingCode2
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise ClassificationCode2
Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and BaselineCode2
HumanBench: Towards General Human-centric Perception with Projector Assisted PretrainingCode2
CNN-based Density Estimation and Crowd Counting: A SurveyCode2
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and LocalizationCode2
EBC-ZIP: Improving Blockwise Crowd Counting with Zero-Inflated Poisson RegressionCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
Free Lunch Enhancements for Multi-modal Crowd CountingCode1
Multi-modal Crowd Counting via a Broker ModalityCode1
The Effectiveness of a Simplified Model Structure for Crowd CountingCode1
Gramformer: Learning Crowd Counting via Graph-Modulated TransformerCode1
Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd ScenesCode1
Boosting Detection in Crowd Analysis via Underutilized Output FeaturesCode1
Point-Query Quadtree for Crowd Counting, Localization, and MoreCode1
DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd CountingCode1
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary TransformerCode1
CLIP-Count: Towards Text-Guided Zero-Shot Object CountingCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception TasksCode1
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion ModelsCode1
Cross-head Supervision for Crowd Counting with Noisy AnnotationsCode1
Super-Resolution Information Enhancement For Crowd CountingCode1
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural NetworksCode1
Improving Deep Regression with Ordinal EntropyCode1
RGB-T Multi-Modal Crowd Counting Based on TransformerCode1
Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised CountingCode1
Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networksCode1
Progressive Multi-resolution Loss for Crowd CountingCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
Spatio-channel Attention Blocks for Cross-modal Crowd CountingCode1
Semi-supervised Crowd Counting via Density AgencyCode1
Redesigning Multi-Scale Neural Network for Crowd CountingCode1
Backdoor Attacks on Crowd CountingCode1
Rethinking Spatial Invariance of Convolutional Networks for Object CountingCode1
Bi-level Alignment for Cross-Domain Crowd CountingCode1
DR.VIC: Decomposition and Reasoning for Video Individual CountingCode1
Boosting Crowd Counting via Multifaceted AttentionCode1
FusionCount: Efficient Crowd Counting via Multiscale Feature FusionCode1
Multiscale Crowd Counting and Localization By Multitask Point SupervisionCode1
Few-shot Object Counting with Similarity-Aware Feature EnhancementCode1
Deep Rank-Consistent Pyramid Model for Enhanced Crowd CountingCode1
Crowd Counting in the Frequency DomainCode1
Panoptic Segmentation: A ReviewCode1
CCTrans: Simplifying and Improving Crowd Counting with TransformerCode1
Harnessing Perceptual Adversarial Patches for Crowd CountingCode1
Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd CountingCode1
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