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Awesome Crowd CountingAwesome

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Contents

Misc

News

  • [2022.09] The VSCrowd Dataset is released.
  • [2022.01] The FUDAN-UCC Dataset is released.
  • [2021.04] The RGBT-CC Benchmark is released.
  • [2020.04] The JHU-CROWD++ Dataset is released.
  • [2020.01] The NWPU-Crowd benchmark is released.

Call for Papers

  • [Electronics] Special Issue on: Recent Advances in Pixel-Wise Image Understanding [Link]. Deadline: November 15, 2023.
  • [Transportation Research Part C] Special Issue on: Applications of artificial intelligence, computer vision, physics and econometrics modelling methods in pedestrian traffic modelling and crowd safety [Link]. Deadline: April 30th, 2023.
  • [IET Image Processing] Special Issue on: Crowd Understanding and Analysis [Link] [PDF]

Challenge

Code

  • [C^3 Framework] An open-source PyTorch code for crowd counting, which is released. GitHub stars
  • [CCLabeler] A web tool for labeling pedestrians in an image, which is released. GitHub stars

Technical blog

  • [Chinese Blog] 人群计数论文解读 [Link]
  • [2019.05] [Chinese Blog] C^3 Framework系列之一:一个基于PyTorch的开源人群计数框架 [Link]
  • [2019.04] Crowd counting from scratch [Link]
  • [2017.11] Counting Crowds and Lines with AI [Link1] [Link2] [Code]GitHub stars

GT generation

Related Tasks

Crowd Analysis, Crowd Localization, Video Surveillance, Dense/Small/Tiny Object Detection

Datasets

Please refer to this page.

Papers

Considering the increasing number of papers in this field, we roughly summarize some articles and put them into the following categories (they are still listed in this document):

[Top Conference/Journal] [Survey] [Un-/semi-/weakly-/self- Supervised Learning]
[Auxiliary Tasks] [Localization] [Transfer Learning and Domain Adaptation]
[Light-weight Models] [Video] [Network Design, Search]
[Perspective Map] [Attention] [Transformer]

arXiv papers

Note that all unpublished arXiv papers are not included in the leaderboard of performance.

  • Scale-Aware Crowd Count Network with Annotation Error Correction [paper]
  • Regressor-Segmenter Mutual Prompt Learning for Crowd Counting [paper]
  • Point, Segment and Count: A Generalized Framework for Object Counting [paper]
  • Learning Discriminative Features for Crowd Counting [paper]
  • Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes [paper]
  • SYRAC: Synthesize, Rank, and Count [paper]
  • Focus for Free in Density-Based Counting [paper]
  • Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement [paper]
  • CLIP-Count: Towards Text-Guided Zero-Shot Object Counting [paper]
  • Can SAM Count Anything? An Empirical Study on SAM Counting [paper]
  • Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications [paper]
  • Crowd Counting with Sparse Annotation [paper]
  • Crowd Counting with Online Knowledge Learning [paper]
  • LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance [paper]
  • GCNet: Probing Self-Similarity Learning for Generalized Counting Network [paper]
  • A Unified Object Counting Network with Object Occupation Prior [paper]
  • Mask Focal Loss for dense crowd counting with canonical object detection networks [paper]
  • CountingMOT: Joint Counting, Detection and Re-Identification for Multiple Object Tracking [paper]
  • Progressive Multi-resolution Loss for Crowd Counting [paper]
  • Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects [paper]
  • Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network [paper]
  • Inception-Based Crowd Counting -- Being Fast while Remaining Accurate [paper]
  • Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training [paper]
  • MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting [paper]
Earlier ArXiv Papers
  • Multi-scale Feature Aggregation for Crowd Counting [paper]
  • Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications [paper]
  • Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes [paper]
  • Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting [paper]
  • Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting [paper]
  • Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches [paper]
  • Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting [paper]
  • Counting with Adaptive Auxiliary Learning [paper][code]GitHub stars
  • CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability [paper]
  • S2FPR: Crowd Counting via Self-Supervised Coarse to Fine Feature Pyramid Ranking [paper][code]GitHub stars
  • Scene-Adaptive Attention Network for Crowd Counting [paper]
  • Object Counting: You Only Need to Look at One [paper]
  • PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting [paper]
  • LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds [paper]
  • CCTrans: Simplifying and Improving Crowd Counting with Transformer [paper]
  • S4-Crowd: Semi-Supervised Learning with Self-Supervised Regularisation for Crowd Counting [paper]
  • Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation [paper]
  • Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting [paper]
  • Multi-Level Attentive Convoluntional Neural Network for Crowd Counting [paper]
  • Boosting Crowd Counting with Transformers [paper]
  • Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification [paper]
  • WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting [paper]
  • Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting [paper]
  • Multi-channel Deep Supervision for Crowd Counting [paper]
  • Enhanced Information Fusion Network for Crowd Counting [paper]
  • Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background [paper]
  • Learning Independent Instance Maps for Crowd Localization [paper] [code]GitHub stars
  • A Strong Baseline for Crowd Counting and Unsupervised People Localization [paper]
  • A Study of Human Gaze Behavior During Visual Crowd Counting [paper]
  • Bayesian Multi Scale Neural Network for Crowd Counting [paper]
  • Dense Crowds Detection and Counting with a Lightweight Architecture [paper]
  • Exploit the potential of Multi-column architecture for Crowd Counting [paper][code]GitHub stars
  • Recurrent Distillation based Crowd Counting [paper]
  • Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions [paper][code]GitHub stars
  • CNN-based Density Estimation and Crowd Counting: A Survey [paper]
  • Drone Based RGBT Vehicle Detection and Counting: A Challenge [paper]
  • Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network [paper][code]
  • Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting [paper]
  • Content-aware Density Map for Crowd Counting and Density Estimation [paper]
  • Crowd Transformer Network [paper]
  • W-Net: Reinforced U-Net for Density Map Estimation [paper][code]GitHub stars
  • Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting [paper]
  • Scale-Aware Attention Network for Crowd Counting [paper]
  • Crowd Counting with Density Adaption Networks [paper]
  • Improving Object Counting with Heatmap Regulation [paper][code]GitHub stars
  • Structured Inhomogeneous Density Map Learning for Crowd Counting [paper]

2024

Conference

  • [Gramformer] Gramformer: Learning Crowd Counting via Graph-Modulated Transformer (WACV)[paper][code]GitHub stars

2023

Conference

  • [SAM] Training-free Object Counting with Prompts (WACV)[paper][code]GitHub stars
  • [SGA] Semantic Generative Augmentations for Few-Shot Counting (WACV)[paper]
  • [SRN] Glance To Count: Learning To Rank With Anchors for Weakly-Supervised Crowd Counting (WACV)[paper][code]GitHub stars
  • [STEERER] STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning (ICCV)[paper][code]GitHub stars
  • [PET] Point-Query Quadtree for Crowd Counting, Localization, and More (ICCV)[paper][code]GitHub stars
  • Striking a Balance: Unsupervised Cross-Domain Crowd Counting via Knowledge Diffusion (ACM MM)[paper]
  • [AWCC-Net] Counting Crowds in Bad Weather (ICCV)[paper][code]GitHub stars
  • [CU] Calibrating Uncertainty for Semi-Supervised Crowd Counting (ICCV)[paper][code]GitHub stars
  • [DAOT] DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting (ACM MM)[paper]
  • [ZSC] Zero-shot Object Counting (CVPR)[paper][code]GitHub stars
  • [DDC] Diffuse-Denoise-Count: Accurate Crowd-Counting with Diffusion Models (CVPR)[paper][code]GitHub stars
  • [IOCFormer] Indiscernible Object Counting in Underwater Scenes (CVPR)[paper][code]GitHub stars
  • [CrowdCLIP] CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model (CVPR)[paper]
  • [OT-M] Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting (CVPR)[paper][code]GitHub stars
  • [DGCC] Domain-general Crowd Counting in Unseen Scenarios (AAAI)[paper] [code]GitHub stars
  • [SAFECount] Few-Shot Object Counting With Similarity-Aware Feature Enhancement (WACV)[paper] [code]GitHub stars
  • [DMCNet] Dynamic Mixture of Counter Network for Location-Agnostic Crowd Counting (WACV)[paper]
  • [CACC] Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation (ICME)[paper]
  • [MSSRM] Super-Resolution Information Enhancement For Crowd Counting (ICASSP)[paper] [code]GitHub stars
  • [CHS-Net] Cross-head Supervision for Crowd Counting with Noisy Annotations (ICASSP)[paper] [code]GitHub stars
  • [Self-ONN] DroneNet: Crowd Density Estimation using Self-ONNs for Drones (CCNC)[paper]

Journal

  • [MDC] Reducing Spatial Labeling Redundancy for Active Semi-supervised Crowd Counting (T-PAMI) [paper]
  • [AGK] Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel (Scientific Reports-Nature) [paper] [code]GitHub stars
  • [GCFL] Generalized Characteristic Function Loss for Crowd Analysis in the Frequency Domain (T-PAMI) [paper]
  • [PESSNet] A Perspective-Embedded Scale-Selection Network for Crowd Counting in Public Transportation (T-ITS) [paper]
  • [MRL] Semi-Supervised Crowd Counting via Multiple Representation Learning (TIP) [paper]
  • [CDENet] Confusion Region Mining for Crowd Counting (T-NNLS) [paper]
  • [FLCC] Federated Learning for Crowd Counting in Smart Surveillance Systems (IEEE IoTJ) [paper]
  • [MGANet] Crowd Counting Based on Multiscale Spatial Guided Perception Aggregation Network (T-NNLS) [paper]
  • [HMoDE] Redesigning Multi-Scale Neural Network for Crowd Counting (TIP) [paper][code]GitHub stars
  • [SS-DCNet] From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting (IJCV) [paper](extension of S-DCNet)
  • [SSL-FT] Self-Supervised Learning with Data-Efficient Supervised Fine-Tuning for Crowd Counting (TMM) [paper]
  • [FRVCC] Frame-Recurrent Video Crowd Counting (T-CSVT) [paper]
  • [FLCB] Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting (FITEE) [paper]
  • [MTCP] Multi-Task Credible Pseudo-Label Learning for Semi-supervised Crowd Counting (T-NNLS) [paper] [code]GitHub stars
  • [STGN] Spatial-Temporal Graph Network for Video Crowd Counting (T-CSVT) [paper] [code]GitHub stars
  • [CmCaF] RGB-D Crowd Counting With Cross-Modal Cycle-Attention Fusion and Fine-Coarse Supervision (TII) [paper]
  • [STC-Crowd] Semi-supervised Crowd Counting with Spatial Temporal Consistency and Pseudo-label Filter (T-CSVT)[paper]
  • [LMSFFNet] A Lightweight Multiscale Feature Fusion Network for Remote Sensing Object Counting (TGRS) [paper]
  • [DDMD] Deformable Density Estimation via Adaptive Representation (TIP) [paper]
  • [UCCF] A unified RGB-T crowd counting learning framework (Image and Vision Computing) [arxiv] [paper]
  • [DASECount] DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning (IEEE IOT) [paper]
  • [CrowdMLP] CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP (Pattern Recognition) [paper]
  • [MTSS] Multi-task semi-supervised crowd counting via global to local self-correction (Pattern Recognition) [paper]

2022

Conference

  • [CSS-CCNN] Completely Self-Supervised Crowd Counting via Distribution Matching (ECCV) [paper][code]GitHub stars
  • [TSFADet] Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection (ECCV) [paper]
  • [CSCA] Spatio-channel Attention Blocks for Cross-modal Crowd Counting (ACCV) [paper] [code]GitHub stars
  • [CUT] Segmentation Assisted U-shaped Multi-scale Transformer for Crowd Counting (BMVC) [paper]
  • [MSDTrans] RGB-T Multi-Modal Crowd Counting Based on Transformer (BMVC)[paper] [code]GitHub stars
  • [LoViTCrowd] Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd Counting (BMVC) [paper] [code]GitHub stars
  • [SPDCN] Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting (BMVC) [paper]
  • [PAP] Harnessing Perceptual Adversarial Patches for Crowd Counting (ACM CCS) [paper] [code]GitHub stars
  • [CLTR] An End-to-End Transformer Model for Crowd Localization (ECCV) [paper] [code]GitHub stars[project]
  • [CF-MVCC] Calibration-free Multi-view Crowd Counting (ECCV) [paper]
  • [DC] Discrete-Constrained Regression for Local Counting Models (ECCV) [paper]
  • [DMBA] Backdoor Attacks on Crowd Counting (ACM MM) [paper][code]GitHub stars
  • [DACount] Semi-supervised-Crowd-Counting-via-Density-Agency (ACM MM) [paper][code]GitHub stars
  • [ChfL] Crowd Counting in the Frequency Domain (CVPR) [paper][code]GitHub stars
  • [GauNet] Rethinking Spatial Invariance of Convolutional Networks for Object Counting (CVPR) [paper][code]GitHub stars
  • [DR.VIC] DR.VIC: Decomposition and Reasoning for Video Individual Counting (CVPR) [paper][code]GitHub stars
  • [CDCC] Leveraging Self-Supervision for Cross-Domain Crowd Counting (CVPR) [paper][code]GitHub stars
  • [MAN] Boosting Crowd Counting via Multifaceted Attention (CVPR) [paper][code]GitHub stars
  • [BLA] Bi-level Alignment for Cross-Domain Crowd Counting (CVPR) [paper][code]GitHub stars
  • [BMNet] Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting (CVPR)[paper][code]GitHub stars
  • Fine-Grained Counting with Crowd-Sourced Supervision (CVPRW) [paper]
  • [CrowdFormer] CrowdFormer: An Overlap Patching Vision Transformer for Top-Down Crowd Counting (IJCAI)[paper]
  • [WSCNN] Single Image Object Counting and Localizing using Active-Learning (WACV) [paper]
  • [IS-Count] IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling (AAAI) [paper][code]GitHub stars
  • [STAN] A Spatio-Temporal Attentive Network for Video-Based Crowd Counting (ISCC) [paper]
  • [LARL] Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar (ICASSP) [paper]
  • [ESA-Net] Enhancing and Dissecting Crowd Counting By Synthetic Data (ICASSP) [paper]
  • [MPS] Multiscale Crowd Counting and Localization By Multitask Point Supervision (ICASSP) [paper][code]GitHub stars
  • [TAFNet] TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting (ISCAS) [paper][code]GitHub stars
  • [HDNet] HDNet: A Hierarchically Decoupled Network for Crowd Counting (ICME) [paper]
  • [SSDA] Self-supervised Domain Adaptation in Crowd Counting (ICIP) [paper]
  • [FusionCount] FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion (ICIP) [paper][code]GitHub stars

Journal

  • [PSGCNet] PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images (TGRS) [paper][code]GitHub stars
  • [MVMS] Wide-Area Crowd Counting: Multi-View Fusion Networks for Counting in Large Scenes (IJCV) [paper](extension of MVMS)
  • [DEFNet] DEFNet: Dual-Branch Enhanced Feature Fusion Network for RGB-T Crowd Counting (TITS) [paper][code]GitHub stars
  • [CLRNet] CLRNet: A Cross Locality Relation Network for Crowd Counting in Videos (T-NNLS) [paper]
  • [AGCCM] Attention-guided Collaborative Counting (TIP) [paper]
  • [GNA] Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark (TIP) [paper][code]GitHub stars
  • [LibraNet+DQN] Counting Crowd by Weighing Counts: A Sequential Decision-Making Perspective (T-NNLS) [paper][code](extension of LibraNet)
  • [FIDTM] Focal Inverse Distance Transform Maps for Crowd Localization (TMM)[paper] [code]GitHub stars [project]
  • [NDConv] An Improved Normed-Deformable Convolution for Crowd Counting (SPL) [paper]
  • [RAN] Region-Aware Network: Model Human’s Top-Down Visual Perception Mechanism for Crowd Counting (Neural Networks) [paper]
  • [HANet] Hybrid attention network based on progressive embedding scale-context for crowd counting (Information Sciences) [paper]
  • [TransCrowd] TransCrowd: Weakly-Supervised Crowd Counting with Transformer (Science China Information Sciences) [paper] [code]GitHub stars
  • [STNet] STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting (TMM) [paper]
  • [SGANet] Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss (TITS) [paper]
  • [CTASNet] Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation (T-CSVT) [paper]
  • [SSR-HEF] SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing (TII) [paper]
  • [ECCNAS] ECCNAS: Efficient Crowd Counting Neural Architecture Search (TOMM) [paper]
  • [SSCC] Scene-specific crowd counting using synthetic training images (Pattern Recognition) [paper]
  • [SL-ViT] Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead (Neural Networks) [paper]
  • [DCST] Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer (Neurocomputing) [paper]
  • A survey on deep learning-based single image crowd counting: Network design, loss function and supervisory signal (Neurocomputing) [paper]

2021

Conference

  • [GNet] Gaussian map predictions for 3D surface feature localisation and counting (BMVC) [paper]
  • [PFSNet] Robust Crowd Counting via Image Enhancement and Dynamic Feature Selection (BMVC) [paper]
  • [URC] Crowd Counting With Partial Annotations in an Image (ICCV) [paper]
  • [MFDC] Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network (ICCV) [paper]
  • [SDNet] Towards A Universal Model for Cross-Dataset Crowd Counting (ICCV) [paper]
  • [P2PNet] Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework (ICCV(Oral)) [paper][code]GitHub stars
  • [UEPNet] Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting (ICCV) [paper][code]GitHub stars
  • [SUA] Spatial Uncertainty-Aware Semi-Supervised Crowd Counting (ICCV) [paper][code]GitHub stars
  • [DKPNet] Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV) [paper][code]GitHub stars
  • [CC-AV] Audio-Visual Transformer Based Crowd Counting (ICCVW) [paper]
  • [BinLoss] Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting (ACM MM) [paper][code]GitHub stars
  • [C2MoT] Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting (ACM MM) [paper][code]GitHub stars
  • [ASNet] Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network (ACM MM) [paper]
  • [APAM] Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting (ACM MM) [paper][code]GitHub stars
  • [S3] Direct Measure Matching for Crowd Counting (IJCAI) [paper]
  • [BM-Count] Bipartite Matching for Crowd Counting with Point Supervision (IJCAI) [paper]
  • [GLoss] A Generalized Loss Function for Crowd Counting and Localization (CVPR) [paper]
  • [CVCS] Cross-View Cross-Scene Multi-View Crowd Counting (CVPR) [paper]
  • [STANet] Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark (CVPR) [paper][code]GitHub stars
  • [RGBT-CC] Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting (CVPR) [paper][code]GitHub stars[Project]
  • [EDIREC-Net] Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting (AAAI) [paper][code]GitHub stars
  • [SASNet] To Choose or to Fuse? Scale Selection for Crowd Counting (AAAI) [paper][code]GitHub stars
  • [UOT] Learning to Count via Unbalanced Optimal Transport (AAAI) [paper]
  • [TopoCount] Localization in the Crowd with Topological Constraints (AAAI) [paper][code]GitHub stars
  • [CFANet] Coarse- and Fine-grained Attention Network with Background-aware Loss for Crowd Density Map Estimation (WACV) [paper][code]GitHub stars
  • [BSCC] Understanding the impact of mistakes on background regions in crowd counting (WACV) [paper]
  • [CFOCNet] Class-agnostic Few-shot Object Counting (WACV) [paper][code]GitHub stars
  • [SCALNet] Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization (ICMEW) [paper][code]GitHub stars
  • [DSNet] Dense Scale Network for Crowd Counting (ICMR) [paper][unofficial code: PyTorch]GitHub stars
  • [FCVF] Learning Factorized Cross-View Fusion for Multi-View Crowd Counting (ICME) [paper]
  • [IDK] Leveraging Intra-Domain Knowledge to Strengthen Cross-Domain Crowd Counting (ICME) [paper]
  • [CRANet] CRANet: Cascade Residual Attention Network for Crowd Counting (ICME) [paper]

Journal

  • [DPDNet] Locating and Counting Heads in Crowds With a Depth Prior (T-PAMI) [paper] [code]GitHub stars
  • [EPF] Counting People by Estimating People Flows (TPAMI) [paper][code]GitHub stars
  • [LA-Batch] Locality-Aware Crowd Counting (TPAMI) [paper]
  • [AutoScale] AutoScale: Learning to Scale for Crowd Counting (IJCV) [paper] (extension of L2SM)[code]GitHub stars
  • [DSACA] Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting (SPL) [paper] [code]GitHub stars
  • [NLT] Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting (T-NNLS) [paper] [code]]GitHub stars
  • [DACC] Domain-Adaptive Crowd Counting via High-Quality Image Translation and Density Reconstruction (T-NNLS) [paper]
  • [MATT] Towards Using Count-level Weak Supervision for Crowd Counting (Pattern Recognition) [paper]
  • [D2C] Decoupled Two-Stage Crowd Counting and Beyond (TIP) [paper][code]GitHub stars
  • [TBC] Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets (TIP) [paper]
  • [FGCC] Fine-Grained Crowd Counting (TIP) [paper]
  • [PSODC] A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds (TIP) [paper][code]GitHub stars
  • [EPA] Embedding Perspective Analysis Into Multi-Column Convolutional Neural Network for Crowd Counting (TIP) [paper]
  • [PFDNet] Crowd Counting via Perspective-Guided Fractional-Dilation Convolution (TMM) [paper](extension of PGCNet)
  • [STDNet] Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation (TMM) [paper]
  • [AdaCrowd] AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting (TMM) [paper][code]GitHub stars
  • [DCANet] Towards Learning Multi-domain Crowd Counting (T-CSVT) [paper] [code]GitHub stars
  • [PDANet] PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting (Neurocomputing) [paper]
  • [ScSiNet] Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting (Neurocomputing) [paper]
  • [PRM] Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network (Neurocomputing) [paper]
  • [DeepCorn] DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation (Knowledge-Based Systems) [paper]

2020

Conference

  • [DM-Count] Distribution Matching for Crowd Counting (NeurIPS) [paper][code]GitHub stars
  • [MNA] Modeling Noisy Annotations for Crowd Counting (NeurIPS) [paper]
  • [SKT] Efficient Crowd Counting via Structured Knowledge Transfer (ACM MM(oral)) [paper][code]GitHub stars
  • [DPN] Learning Scales from Points: A Scale-aware Probabilistic Model for Crowd Counting (ACM MM(oral)) [paper]
  • [RDBT] Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer (ACM MM) [paper]
  • [VisDrone-CC2020] VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results (ECCV) [paper]
  • [EPF] Estimating People Flows to Better Count Them in Crowded Scenes (ECCV) [paper][code]GitHub stars
  • [AMSNet] NAS-Count: Counting-by-Density with Neural Architecture Search (ECCV) [paper]
  • [AMRNet] Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting (ECCV) [paper][code]GitHub stars
  • [LibraNet] Weighting Counts: Sequential Crowd Counting by Reinforcement Learning (ECCV) [paper][code]GitHub stars
  • [GP] Learning to Count in the Crowd from Limited Labeled Data (ECCV) [paper]
  • [IRAST] Semi-supervised Crowd Counting via Self-training on Surrogate Tasks (ECCV) [paper]
  • [PSSW] Active Crowd Counting with Limited Supervision (ECCV) [paper]
  • [CCLS] Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations (ECCV) [paper]
  • [Bi-pathNet] A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View (ECCVW) [paper]
  • [ADSCNet] Adaptive Dilated Network with Self-Correction Supervision for Counting (CVPR) [paper]
  • [RPNet] Reverse Perspective Network for Perspective-Aware Object Counting (CVPR) [paper] [code]
  • [ASNet] Attention Scaling for Crowd Counting (CVPR) [paper] [code]GitHub stars
  • [SRF-Net] Scale-Aware Rolling Fusion Network for Crowd Counting (ICME) [paper]
  • [EDC] Learning Error-Driven Curriculum for Crowd Counting (ICPR) [paper][code]GitHub stars
  • [PRM] Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting (ICPR) [paper]
  • [M-SFANet] Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting (ICPR) [paper][code]GitHub stars
  • [HSRNet] Crowd Counting via Hierarchical Scale Recalibration Network (ECAI) [paper]
  • [DeepCount] Deep Density-aware Count Regressor (ECAI) [paper][code]GitHub stars
  • [SOFA-Net] SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting (BMVC) [paper]
  • [CWAN] Weakly Supervised Crowd-Wise Attention For Robust Crowd Counting (ICASSP) [paper]
  • [AGRD] Attention Guided Region Division for Crowd Counting (ICASSP) [paper]
  • [BBA-NET] BBA-NET: A Bi-Branch Attention Network For Crowd Counting (ICASSP) [paper]
  • [SMANet] Stochastic Multi-Scale Aggregation Network for Crowd Counting (ICASSP) [paper]
  • [Stacked-Pool] Stacked Pooling For Boosting Scale Invariance Of Crowd Counting (ICASSP) [paper] [arxiv] [code]GitHub stars
  • [MSPNET] Multi-supervised Parallel Network for Crowd Counting (ICASSP) [paper]
  • [ASPDNet] Counting dense objects in remote sensing images (ICASSP) [paper]
  • [FSC] Focus on Semantic Consistency for Cross-domain Crowd Understanding (ICASSP) [paper]
  • [C-CNN] A Real-Time Deep Network for Crowd Counting (ICASSP) [arxiv][ieee]
  • [HyGnn] Hybrid Graph Neural Networks for Crowd Counting (AAAI) [paper]
  • [DUBNet] Crowd Counting with Decomposed Uncertainty (AAAI) [paper]
  • [SDANet] Shallow Feature based Dense Attention Network for Crowd Counting (AAAI) [paper]
  • [3DCC] 3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels (AAAI) [paper][Project]
  • [FSSA] Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning (WACV) [paper][code] GitHub stars
  • [CC-Mod] Plug-and-Play Rescaling Based Crowd Counting in Static Images (WACV) [paper]
  • [CTN] Uncertainty Estimation and Sample Selection for Crowd Counting (ACCV) [paper]
  • [ikNN] Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling (VISAPP) [paper]

Journal

  • [NWPU] NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization (T-PAMI) [paper][code]GitHub stars
  • [KDMG] Kernel-based Density Map Generation for Dense Object Counting (T-PAMI) [paper][code]GitHub stars
  • [JHU-CROWD] JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (T-PAMI) [paper](extension of CG-DRCN)
  • [LSC-CNN] Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection (T-PAMI) [paper][code]GitHub stars
  • [PWCU] Pixel-wise Crowd Understanding via Synthetic Data (IJCV) [paper]GitHub stars
  • [CRNet] Crowd Counting via Cross-stage Refinement Networks (TIP) [paper][code] GitHub stars
  • [BNFDD] Background Noise Filtering and Distribution Dividing for Crowd Counting (TIP) [paper]
  • [FADA] Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance (TCYB) [paper]
  • [MS-GAN] Adversarial Learning for Multiscale Crowd Counting Under Complex Scenes (TCYB) [paper]
  • [DCL] Density-aware Curriculum Learning for Crowd Counting (TCYB) [paper][code]GitHub stars
  • [ZoomCount] ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images (T-CSVT) [paper]
  • [DensityCNN] Density-Aware Multi-Task Learning for Crowd Counting (TMM) [paper]
  • [DENet] DENet: A Universal Network for Counting Crowd with Varying Densities and Scales (TMM) [paper][code]GitHub stars
  • [CLPNet] Cross-Level Parallel Network for Crowd Counting (TII) [paper]
  • [FMLF] Crowd Density Estimation Using Fusion of Multi-Layer Features (TITS) [paper]
  • [MLSTN] Multi-level feature fusion based Locality-Constrained Spatial Transformer network for video crowd counting (Neurocomputing) [paper](extension of LSTN)
  • [SRN+PS] Scale-Recursive Network with point supervision for crowd scene analysis (Neurocomputing) [paper]
  • [ASDF] Counting crowds with varying densities via adaptive scenario discovery framework (Neurocomputing) [paper](extension of ASD)
  • [CAT-CNN] Crowd counting with crowd attention convolutional neural network (Neurocomputing) [paper]
  • [RRP] Relevant Region Prediction for Crowd Counting (Neurocomputing) [paper]
  • [SCAN] Crowd Counting via Scale-Communicative Aggregation Networks (Neurocomputing) [paper](extension of MVSAN)
  • [MobileCount] MobileCount: An Efficient Encoder-Decoder Framework for Real-Time Crowd Counting (Neurocomputing) [conference paper] [journal paper] [code]GitHub stars
  • [TAN] Fast Video Crowd Counting with a Temporal Aware Network (Neurocomputing) [paper]
  • [MH-METRONET] MH-MetroNet—A Multi-Head CNN for Passenger-Crowd Attendance Estimation (JImaging) [paper][code]

2019

Conference

  • [CG-DRCN] Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method (ICCV)[paper]
  • [ADMG] Adaptive Density Map Generation for Crowd Counting (ICCV)[paper]
  • [DSSINet] Crowd Counting with Deep Structured Scale Integration Network (ICCV) [paper][code] GitHub stars
  • [RANet] Relational Attention Network for Crowd Counting (ICCV)[paper]
  • [ANF] Attentional Neural Fields for Crowd Counting (ICCV)[paper]
  • [SPANet] Learning Spatial Awareness to Improve Crowd Counting (ICCV(oral)) [paper]
  • [MBTTBF] Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting (ICCV) [paper]
  • [CFF] Counting with Focus for Free (ICCV) [paper][code] GitHub stars
  • [L2SM] Learn to Scale: Generating Multipolar Normalized Density Map for Crowd Counting (ICCV) [paper]
  • [S-DCNet] From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer (ICCV) [paper][code]GitHub stars
  • [BL] Bayesian Loss for Crowd Count Estimation with Point Supervision (ICCV(oral)) [paper][code] GitHub stars
  • [PGCNet] Perspective-Guided Convolution Networks for Crowd Counting (ICCV) [paper][code]GitHub stars
  • [SACANet] Crowd Counting on Images with Scale Variation and Isolated Clusters (ICCVW) [paper]
  • [McML] Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting (ACM MM) [paper]
  • [DADNet] DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting (ACM MM) [paper]
  • [MRNet] Crowd Counting via Multi-layer Regression (ACM MM) [paper]
  • [MRCNet] MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery (BMVCW)[paper]
  • [E3D] Enhanced 3D convolutional networks for crowd counting (BMVC) [paper]
  • [OSSS] One-Shot Scene-Specific Crowd Counting (BMVC) [paper]
  • [RAZ-Net] Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization (CVPR) [paper]
  • [RDNet] Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization (CVPR) [paper][code] GitHub stars
  • [RRSP] Residual Regression with Semantic Prior for Crowd Counting (CVPR) [paper][code] GitHub stars
  • [MVMS] Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs (CVPR) [paper] [Project] [Dataset&Code]
  • [AT-CFCN] Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting (CVPR) [paper]
  • [TEDnet] Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (CVPR) [paper]
  • [CAN] Context-Aware Crowd Counting (CVPR) [paper] [code]GitHub stars
  • [PACNN] Revisiting Perspective Information for Efficient Crowd Counting (CVPR)[paper]
  • [PSDDN] Point in, Box out: Beyond Counting Persons in Crowds (CVPR(oral))[paper]
  • [ADCrowdNet] ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding (CVPR) [paper]
  • [CCWld, SFCN] Learning from Synthetic Data for Crowd Counting in the Wild (CVPR) [paper] [Project] [arxiv] GitHub stars
  • [DG-GAN] Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks (CVPRW)[paper]
  • [GSP] Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images (CVPRW)[paper]
  • [IA-DNN] Inverse Attention Guided Deep Crowd Counting Network (AVSS Best Paper) [paper]
  • [MTCNet] MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation (AVSS) [paper]
  • [CODA] CODA: Counting Objects via Scale-aware Adversarial Density Adaption (ICME) [paper][code]GitHub stars
  • [LSTN] Locality-Constrained Spatial Transformer Network for Video Crowd Counting (ICME(oral)) [paper]
  • [DRD] Dynamic Region Division for Adaptive Learning Pedestrian Counting (ICME) [paper]
  • [MVSAN] Crowd Counting via Multi-View Scale Aggregation Networks (ICME) [paper]
  • [ASD] Adaptive Scenario Discovery for Crowd Counting (ICASSP) [paper]
  • [SAAN] Crowd Counting Using Scale-Aware Attention Networks (WACV) [paper]
  • [SPN] Scale Pyramid Network for Crowd Counting (WACV) [paper]
  • [GWTA-CCNN] Almost Unsupervised Learning for Dense Crowd Counting (AAAI) [paper]
  • [GPC] Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation (IROS) [paper]
  • [AM-CNN] Attention to Head Locations for Crowd Counting (ICIG) [paper]
  • [CRDNet] Cascaded Residual Density Network for Crowd Counting (ICIP) [paper]

Journal

  • [D-ConvNet] Nonlinear Regression via Deep Negative Correlation Learning (T-PAMI) [paper](extension of D-ConvNet)[Project]
  • [SL2R] Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank (T-PAMI) [paper](extension of L2R)
  • [PCC-Net] PCC Net: Perspective Crowd Counting via Spatial Convolutional Network (T-CSVT) [paper] [code]GitHub stars
  • [Deem] Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural Networks (T-CSVT) [paper]
  • [CLPC] Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation (T-CSVT) [paper]
  • [MAN] Mask-aware networks for crowd counting (T-CSVT) [paper]
  • Generalizing semi-supervised generative adversarial networks to regression using feature contrasting (CVIU)[paper]
  • [CCLL] Crowd Counting With Limited Labeling Through Submodular Frame Selection (T-ITS) [paper]
  • [GMLCNN] Learning Multi-Level Density Maps for Crowd Counting (T-NNLS) [paper]
  • [HA-CCN] HA-CCN: Hierarchical Attention-based Crowd Counting Network (TIP) [paper]
  • [PaDNet] PaDNet: Pan-Density Crowd Counting (TIP) [paper]
  • [LDL] Indoor Crowd Counting by Mixture of Gaussians Label Distribution Learning (TIP) [paper]
  • [ACSPNet] Atrous convolutions spatial pyramid network for crowd counting and density estimation (Neurocomputing) [paper]
  • [DDCN] Removing background interference for crowd counting via de-background detail convolutional network (Neurocomputing) [paper]
  • [MRA-CNN] Multi-resolution attention convolutional neural network for crowd counting (Neurocomputing) [paper]
  • [ACM-CNN] Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs (Neurocomputing) [paper]
  • [SDA-MCNN] Counting crowds using a scale-distribution-aware network and adaptive human-shaped kernel (Neurocomputing) [paper]
  • [SCAR] SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting (Neurocomputing) [paper][code]GitHub stars

2018

Conference

  • [SANet] Scale Aggregation Network for Accurate and Efficient Crowd Counting (ECCV) [paper]
  • [ic-CNN] Iterative Crowd Counting (ECCV) [paper]
  • [CL] Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (ECCV) [paper]
  • [LCFCN] Where are the Blobs: Counting by Localization with Point Supervision (ECCV) [paper] [code]GitHub stars
  • [CSR] CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (CVPR) [paper] [code]GitHub stars
  • [L2R] Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR) [paper] [code] GitHub stars
  • [ACSCP] Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (CVPR) [paper] [unofficial code: PyTorch]GitHub stars
  • [DecideNet] DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density (CVPR) [paper]
  • [AMDCN] An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting (CVPRW) [paper] [code] GitHub stars
  • [D-ConvNet] Crowd Counting with Deep Negative Correlation Learning (CVPR) [paper] [code]GitHub stars
  • [IG-CNN] Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN (CVPR) [paper]
  • [SCNet] In Defense of Single-column Networks for Crowd Counting (BMVC) [paper]
  • [AFP] Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid (BMVC) [paper]
  • [DRSAN] Crowd Counting using Deep Recurrent Spatial-Aware Network (IJCAI) [paper]
  • [TDF-CNN] Top-Down Feedback for Crowd Counting Convolutional Neural Network (AAAI) [paper]
  • [CAC] Class-Agnostic Counting (ACCV) [paper] [code]GitHub stars
  • [A-CCNN] A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting (ICIP) [paper]
  • Crowd Counting with Fully Convolutional Neural Network (ICIP) [paper]
  • [MS-GAN] Multi-scale Generative Adversarial Networks for Crowd Counting (ICPR) [paper]
  • [DR-ResNet] A Deeply-Recursive Convolutional Network for Crowd Counting (ICASSP) [paper]
  • [GAN-MTR] Crowd Counting With Minimal Data Using Generative Adversarial Networks For Multiple Target Regression (WACV) [paper]
  • [SaCNN] Crowd counting via scale-adaptive convolutional neural network (WACV) [paper] [code]GitHub stars

Journal

  • [BSAD] Body Structure Aware Deep Crowd Counting (TIP) [paper]
  • [NetVLAD] Multiscale Multitask Deep NetVLAD for Crowd Counting (TII) [paper] [code]GitHub stars
  • [W-VLAD] Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps (T-CSVT) [paper]
  • [Improved SaCNN] Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network (IEEE Access) [paper]
  • [DA-Net] DA-Net: Learning the Fine-Grained Density Distribution With Deformation Aggregation Network (IEEE Access) [paper][code]GitHub stars

2017

Conference

  • [Switching CNN] Switching Convolutional Neural Network for Crowd Counting (CVPR) [paper] [code]GitHub stars
  • [CP-CNN] Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs (ICCV) [paper]
  • [ConvLSTM] Spatiotemporal Modeling for Crowd Counting in Videos (ICCV) [paper]
  • [CMTL] CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (AVSS) [paper] [code]GitHub stars
  • [ResnetCrowd] ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification (AVSS) [paper]
  • [ACNN] Incorporating Side Information by Adaptive Convolution (NeurIPS) [paper][Project]
  • [MSCNN] Multi-scale Convolution Neural Networks for Crowd Counting (ICIP) [paper] [code]GitHub stars
  • [FCNCC] Fully Convolutional Crowd Counting On Highly Congested Scenes (VISAPP) [paper]

Journal

  • [DAL-SVR] Boosting deep attribute learning via support vector regression for fast moving crowd counting (PR Letters) [paper]
  • [CNN-MRF] Image Crowd Counting Using Convolutional Neural Network and Markov Random Field (JACII) [paper] [code]GitHub stars

2016

Conference

  • [MCNN] Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (CVPR) [paper] [unofficial code: TensorFlow GitHub stars PyTorch]GitHub stars
  • [Hydra-CNN] Towards perspective-free object counting with deep learning (ECCV) [paper] [code]GitHub stars
  • [CNN-Boosting] Learning to Count with CNN Boosting (ECCV) [paper]
  • [Crossing-line] Crossing-line Crowd Counting with Two-phase Deep Neural Networks (ECCV) [paper]
  • [GP] Gaussian Process Density Counting from Weak Supervision (ECCV) [paper]
  • [CrowdNet] CrowdNet: A Deep Convolutional Network for Dense Crowd Counting (ACMMM) [paper] [code]GitHub stars
  • [Shang 2016] End-to-end crowd counting via joint learning local and global count (ICIP) [paper]
  • [DE-VOC] Fast visual object counting via example-based density estimation (ICIP) [paper]
  • [RPF] Crowd Density Estimation based on Rich Features and Random Projection Forest (WACV) [paper]
  • [CS-SLR] Cost-sensitive sparse linear regression for crowd counting with imbalanced training data (ICME) [paper]
  • [Faster-OHEM-KCF] Deep People Counting with Faster R-CNN and Correlation Tracking (ICME) [paper]

2015

Conference

  • [COUNT Forest] COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation (ICCV) [paper]
  • [Bayesian] Bayesian Model Adaptation for Crowd Counts (ICCV) [paper]
  • [Zhang 2015] Cross-scene Crowd Counting via Deep Convolutional Neural Networks (CVPR) [paper] [code]GitHub stars
  • [Wang 2015] Deep People Counting in Extremely Dense Crowds (ACMMM) [paper]

Journal

  • [FU 2015] Fast crowd density estimation with convolutional neural networks (Artificial Intelligence) [paper]

2014

Conference

  • [Arteta 2014] Interactive Object Counting (ECCV) [paper]

2013

Conference

  • [Idrees 2013] Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images (CVPR) [paper]
  • [Ma 2013] Crossing the Line: Crowd Counting by Integer Programming with Local Features (CVPR) [paper]
  • [Chen 2013] Cumulative Attribute Space for Age and Crowd Density Estimation (CVPR) [paper]
  • [SSR] From Semi-Supervised to Transfer Counting of Crowds (ICCV) [paper]

2012

Conference

  • [Chen 2012] Feature mining for localised crowd counting (BMVC) [paper]

2011

Conference

  • [Rodriguez 2011] Density-aware person detection and tracking in crowds (ICCV) [paper]

2010

Conference

  • [Lempitsky 2010] Learning To Count Objects in Images (NeurIPS) [paper]

2008

Conference

  • [Chan 2008] Privacy preserving crowd monitoring: Counting people without people models or tracking (CVPR) [paper]

Leaderboard

The section is being continually updated. Note that some values have superscript, which indicates their source.

NWPU

Please refer to this page.

ShanghaiTech Part A

Year-Conference/Journal Methods MAE MSE PSNR SSIM Params Pre-trained Model
2016--CVPR MCNN 110.2 173.2 21.4CSR 0.52CSR 0.13MSANet None
2017--AVSS CMTL 101.3 152.4 - - - None
2017--CVPR Switching CNN 90.4 135.0 - - 15.11MSANet VGG-16
2017--ICIP MSCNN 83.8 127.4 - - - -
2017--ICCV CP-CNN 73.6 106.4 21.72CP-CNN 0.72CP-CNN 68.4MSANet -
2018--AAAI TDF-CNN 97.5 145.1 - - - -
2018--WACV SaCNN 86.8 139.2 - - - -
2018--CVPR ACSCP 75.7 102.7 - - 5.1M None
2018--CVPR D-ConvNet-v1 73.5 112.3 - - - VGG-16
2018--CVPR IG-CNN 72.5 118.2 - - - VGG-16
2018--CVPR L2R (Multi-task, Query-by-example) 72.0 106.6 - - - VGG-16
2018--CVPR L2R (Multi-task, Keyword) 73.6 112.0 - - - VGG-16
2019--CVPRW GSP (one stage, efficient) 70.7 103.6 - - - VGG-16
2018--IJCAI DRSAN 69.3 96.4 - - - -
2018--ECCV ic-CNN (one stage) 69.8 117.3 - - - -
2018--ECCV ic-CNN (two stages) 68.5 116.2 - - - -
2018--CVPR CSRNet 68.2 115.0 23.79 0.76 16.26MSANet VGG-16
2018--ECCV SANet 67.0 104.5 - - 0.91M None
2019--AAAI GWTA-CCNN 154.7 229.4 - - - -
2021--TPAMI LA-Batch (backbone CSRNet) 65.8 103.6 - - - -
2019--ICASSP ASD 65.6 98.0 - - - -
2019--ICCV CFF 65.2 109.4 25.4 0.78 - -
2019--CVPR SFCN 64.8 107.5 - - - -
2020--AAAI DUBNet 64.6 106.8 - - - -
2019--ICCV SPN+L2SM 64.2 98.4 - - - -
2019--CVPR TEDnet 64.2 109.1 25.88 0.83 1.63M -
2019--CVPR ADCrowdNet(AMG-bAttn-DME) 63.2 98.9 24.48 0.88 - -
2019--CVPR PACNN 66.3 106.4 - - - -
2019--CVPR PACNN+CSRNet 62.4 102.0 - - - -
2019--CVPR CAN 62.3 100.0 - - - VGG-16
2019--TIP HA-CCN 62.9 94.9 - - - -
2019--ICCV BL 62.8 101.8 - - - -
2019--WACV SPN 61.7 99.5 - - - -
2019--ICCV DSSINet 60.63 96.04 - - - -
2019--ICCV MBTTBF-SCFB 60.2 94.1 - - - -
2019--ICCV RANet 59.4 102.0 - - - -
2019--ICCV SPANet+SANet 59.4 92.5 - - - -
2019--TIP PaDNet 59.2 98.1 - - - -
2022--CVPR GauNet 59.2 95.4 - - - VGG-16
2019--ICCV S-DCNet 58.3 95.0 - - - -
2020--ICPR M-SFANet+M-SegNet 57.55 94.48 - - - -
2019--ICCV PGCNet 57.0 86.0 - - - -
2020--ECCV AMSNet 56.7 93.4 - - - -
2020--CVPR ADSCNet 55.4 97.7 - - - -
2021--AAAI SASNet 53.59 88.38 - - - -

ShanghaiTech Part B

Year-Conference/Journal Methods MAE MSE
2016--CVPR MCNN 26.4 41.3
2017--ICIP MSCNN 17.7 30.2
2017--AVSS CMTL 20.0 31.1
2017--CVPR Switching CNN 21.6 33.4
2017--ICCV CP-CNN 20.1 30.1
2018--TIP BSAD 20.2 35.6
2018--WACV SaCNN 16.2 25.8
2018--CVPR ACSCP 17.2 27.4
2018--CVPR CSRNet 10.6 16.0
2018--CVPR IG-CNN 13.6 21.1
2018--CVPR D-ConvNet-v1 18.7 26.0
2018--CVPR DecideNet 21.53 31.98
2018--CVPR DecideNet + R3 20.75 29.42
2018--CVPR L2R (Multi-task, Query-by-example) 14.4 23.8
2018--CVPR L2R (Multi-task, Keyword) 13.7 21.4
2018--IJCAI DRSAN 11.1 18.2
2018--AAAI TDF-CNN 20.7 32.8
2018--ECCV ic-CNN (one stage) 10.4 16.7
2018--ECCV ic-CNN (two stages) 10.7 16.0
2019--CVPRW GSP (one stage, efficient) 9.1 15.9
2021--TPAMI LA-Batch (backbone CSRNet) 8.6 13.6
2018--ECCV SANet 8.4 13.6
2019--WACV SPN 9.4 14.4
2019--ICCV PGCNet 8.8 13.7
2019--ICASSP ASD 8.5 13.7
2019--CVPR TEDnet 8.2 12.8
2019--TIP HA-CCN 8.1 13.4
2019--TIP PaDNet 8.1 12.2
2019--ICCV RANet 7.9 12.9
2019--CVPR CAN 7.8 12.2
2019--CVPR ADCrowdNet(AMG-attn-DME) 7.7 12.9
2020--AAAI DUBNet 7.7 12.5
2019--CVPR ADCrowdNet(AMG-DME) 7.6 13.9
2019--CVPR SFCN 7.6 13.0
2019--CVPR PACNN 8.9 13.5
2022--CVPR GauNet(VGG-16) 7.6 12.7
2019--CVPR PACNN+CSRNet 7.6 11.8
2019--ICCV BL 7.7 12.7
2019--ICCV CFF 7.2 12.2
2019--ICCV SPN+L2SM 7.2 11.1
2019--ICCV DSSINet 6.85 10.34
2019--ICCV S-DCNet 6.7 10.7
2019--ICCV SPANet+SANet 6.5 9.9
2020--CVPR ADSCNet 6.4 11.3
2020--ICPR M-SFANet+M-SegNet 6.32 10.06
2021--AAAI SASNet 6.35 9.9

JHU-CROWD++

Year-Conference/Journal Methods MAE(Val Set) MSE(Val Set) MAE(Test Set) MSE(Test Set)
2016--CVPR MCNN 160.6 377.7 188.9 483.4
2017--AVSS CMTL 138.1 379.5 157.8 490.4
2019--ICCV DSSINet 116.6 317.4 133.5 416.5
2019--CVPR CAN 89.5 239.3 100.1 314.0
2020--TPAMI LSC-CNN 87.3 309.0 112.7 454.4
2018--ECCV SANet 82.1 272.6 91.1 320.4
2019--ICCV MBTTBF 73.8 256.8 81.8 299.1
2018--CVPR CSRNet 72.2 249.9 85.9 309.2
2022--CVPR GauNet(VGG-16) - - 69.4 262.4
2020--TPAMI CG-DRCN-CC-VGG16 67.9 262.1 82.3 328.0
2019--CVPR SFCN 62.9 247.5 77.5 297.6
2019--ICCV BL 59.3 229.2 75.0 299.9
2020--TPAMI CG-DRCN-CC-Res101 57.6 244.4 71.0 278.6

UCF-QNRF

Year-Conference/Journal Method C-MAE C-NAE C-MSE DM-MAE DM-MSE DM-HI L- Av. Precision L-Av. Recall L-AUC
2013--CVPR Idrees 2013CL 315 0.63 508 - - - - - -
2016--CVPR MCNNCL 277 0.55 426 0.006670 0.0223 0.5354 59.93% 63.50% 0.591
2017--AVSS CMTLCL 252 0.54 514 0.005932 0.0244 0.5024 - - -
2017--CVPR Switching CNNCL 228 0.44 445 0.005673 0.0263 0.5301 - - -
2018--ECCV CL 132 0.26 191 0.00044 0.0017 0.9131 75.8% 59.75% 0.714
2019--TIP HA-CCN 118.1 - 180.4 - - - - - -
2019--CVPR TEDnet 113 - 188 - - - - - -
2021--TPAMI LA-Batch 113 - 210 - - - - - -
2019--ICCV RANet 111 - 190 - - - - - -
2019--CVPR CAN 107 - 183 - - - - - -
2020--AAAI DUBNet 105.6 - 180.5 - - - - - -
2019--ICCV SPN+L2SM 104.7 - 173.6 - - - - - -
2019--ICCV S-DCNet 104.4 - 176.1 - - - - - -
2019--CVPR SFCN 102.0 - 171.4 - - - - - -
2019--ICCV DSSINet 99.1 - 159.2 - - - - - -
2019--ICCV MBTTBF-SCFB 97.5 - 165.2 - - - - - -
2019--TIP PaDNet 96.5 - 170.2 - - - - - -
2019--ICCV BL 88.7 - 154.8 - - - - - -
2020--ICPR M-SFANet 85.6 - 151.23 - - - - - -
2021--AAAI SASNet 85.2 - 147.3 - - - - - -
2022--CVPR GauNet(VGG-16) 84.2 - 152.4 - - - - - -
2020--CVPR ADSCNet 71.3 - 132.5 - - - - - -

UCF_CC_50

Year-Conference/Journal Methods MAE MSE
2013--CVPR Idrees 2013 468.0 590.3
2015--CVPR Zhang 2015 467.0 498.5
2016--ACM MM CrowdNet 452.5 -
2016--CVPR MCNN 377.6 509.1
2016--ECCV CNN-Boosting 364.4 -
2016--ECCV Hydra-CNN 333.73 425.26
2016--ICIP Shang 2016 270.3 -
2017--ICIP MSCNN 363.7 468.4
2017--AVSS CMTL 322.8 397.9
2017--CVPR Switching CNN 318.1 439.2
2017--ICCV CP-CNN 298.8 320.9
2017--ICCV ConvLSTM-nt 284.5 297.1
2018--TIP BSAD 409.5 563.7
2018--AAAI TDF-CNN 354.7 491.4
2018--WACV SaCNN 314.9 424.8
2018--CVPR IG-CNN 291.4 349.4
2018--CVPR ACSCP 291.0 404.6
2018--CVPR L2R (Multi-task, Query-by-example) 291.5 397.6
2018--CVPR L2R (Multi-task, Keyword) 279.6 388.9
2018--CVPR D-ConvNet-v1 288.4 404.7
2018--CVPR CSRNet 266.1 397.5
2018--ECCV ic-CNN (two stages) 260.9 365.5
2018--ECCV SANet 258.4 334.9
2018--IJCAI DRSAN 219.2 250.2
2019--AAAI GWTA-CCNN 433.7 583.3
2019--WACV SPN 259.2 335.9
2019--CVPR ADCrowdNet(DME) 257.1 363.5
2019--TIP HA-CCN 256.2 348.4
2019--CVPR TEDnet 249.4 354.5
2019--CVPR PACNN 267.9 357.8
2020--AAAI DUBNet 243.8 329.3
2019--CVPR PACNN+CSRNet 241.7 320.7
2019--ICCV RANet 239.8 319.4
2019--ICCV MBTTBF-SCFB 233.1 300.9
2019--ICCV BL 229.3 308.2
2019--ICCV DSSINet 216.9 302.4
2022--CVPR GauNet(VGG-16) 215.4 296.4
2019--CVPR SFCN 214.2 318.2
2019--CVPR CAN 212.2 243.7
2019--ICCV S-DCNet 204.2 301.3
2021--TPAMI LA-Batch (backbone CSRNet) 203.0 230.6
2019--ICASSP ASD 196.2 270.9
2019--ICCV SPN+L2SM 188.4 315.3
2019--TIP PaDNet 185.8 278.3
2020--ICPR M-SFANet 162.33 276.76
2021--AAAI SASNet 161.4 234.46

WorldExpo'10

Year-Conference/Journal Method S1 S2 S3 S4 S5 Avg.
2015--CVPR Zhang 2015 9.8 14.1 14.3 22.2 3.7 12.9
2016--CVPR MCNN 3.4 20.6 12.9 13.0 8.1 11.6
2017--ICIP MSCNN 7.8 15.4 14.9 11.8 5.8 11.7
2017--ICCV ConvLSTM-nt 8.6 16.9 14.6 15.4 4.0 11.9
2017--ICCV ConvLSTM 7.1 15.2 15.2 13.9 3.5 10.9
2017--ICCV Bidirectional ConvLSTM 6.8 14.5 14.9 13.5 3.1 10.6
2017--CVPR Switching CNN 4.4 15.7 10.0 11.0 5.9 9.4
2017--ICCV CP-CNN 2.9 14.7 10.5 10.4 5.8 8.86
2018--AAAI TDF-CNN 2.7 23.4 10.7 17.6 3.3 11.5
2018--CVPR IG-CNN 2.6 16.1 10.15 20.2 7.6 11.3
2018--TIP BSAD 4.1 21.7 11.9 11.0 3.5 10.5
2018--ECCV ic-CNN 17.0 12.3 9.2 8.1 4.7 10.3
2018--CVPR DecideNet 2.0 13.14 8.9 17.4 4.75 9.23
2018--CVPR D-ConvNet-v1 1.9 12.1 20.7 8.3 2.6 9.1
2018--CVPR CSRNet 2.9 11.5 8.6 16.6 3.4 8.6
2018--WACV SaCNN 2.6 13.5 10.6 12.5 3.3 8.5
2018--ECCV SANet 2.6 13.2 9.0 13.3 3.0 8.2
2018--IJCAI DRSAN 2.6 11.8 10.3 10.4 3.7 7.76
2018--CVPR ACSCP 2.8 14.05 9.6 8.1 2.9 7.5
2019--ICCV PGCNet 2.5 12.7 8.4 13.7 3.2 8.1
2021--TPAMI LA-Batch(backbone CSRNet) 2.4 11.0 8.1 13.5 2.7 7.5
2019--CVPR TEDnet 2.3 10.1 11.3 13.8 2.6 8.0
2019--CVPR PACNN 2.3 12.5 9.1 11.2 3.8 7.8
2019--CVPR ADCrowdNet(AMG-bAttn-DME) 1.7 14.4 11.5 7.9 3.0 7.7
2019--CVPR ADCrowdNet(AMG-attn-DME) 1.6 13.2 8.7 10.6 2.6 7.3
2019--CVPR CAN 2.9 12.0 10.0 7.9 4.3 7.4
2019--CVPR CAN(ECAN) 2.4 9.4 8.8 11.2 4.0 7.2
2019--ICCV DSSINet 1.57 9.51 9.46 10.35 2.49 6.67
2020--ICPR M-SFANet 1.88 13.24 10.07 7.5 3.87 7.32
2020--CVPR ASNet 2.22 10.11 8.89 7.14 4.84 6.64
2021--AAAI SASNet 1.134 13.24 7.68 7.61 2.07 5.71

UCSD

Year-Conference/Journal Method MAE MSE
2015--CVPR Zhang 2015 1.60 3.31
2016--ECCV Hydra-CNN 1.65 -
2016--ECCV CNN-Boosting 1.10 -
2016--CVPR MCNN 1.07 1.35
2017--ICCV ConvLSTM-nt 1.73 3.52
2017--CVPR Switching CNN 1.62 2.10
2017--ICCV ConvLSTM 1.30 1.79
2017--ICCV Bidirectional ConvLSTM 1.13 1.43
2018--CVPR CSRNet 1.16 1.47
2018--CVPR ACSCP 1.04 1.35
2018--ECCV SANet 1.02 1.29
2018--TIP BSAD 1.00 1.40
2019--WACV SPN 1.03 1.32
2019--ICCV SPANet+SANet 1.00 1.28
2019--CVPR ADCrowdNet(DME) 0.98 1.25
2019--BMVC E3D 0.93 1.17
2019--CVPR PACNN 0.89 1.18
2019--TIP PaDNet 0.85 1.06

Mall

Year-Conference/Journal Method MAE MSE
2012--BMVC Chen 2012 3.15 15.7
2016--ECCV CNN-Boosting 2.01 -
2017--ICCV ConvLSTM-nt 2.53 11.2
2017--ICCV ConvLSTM 2.24 8.5
2017--ICCV Bidirectional ConvLSTM 2.10 7.6
2018--CVPR DecideNet 1.52 1.90
2018--IJCAI DRSAN 1.72 2.1
2019--BMVC E3D 1.64 2.13
2021--TPAMI LA-Batch (backbone CSRNet) 1.34 1.60
2019--WACV SAAN 1.28 1.68

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