Awesome Crowd Counting

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Contents

• Misc
• Datasets
• Papers
• Leaderboard

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

• [NWPU-Crowd Counting] Crowd counting. Deadline: none.
• [VisDrone 2021] Crowd counting. ICCV Workshop. Deadline: 2021.07.15.
• [VisDrone 2020] Crowd counting. ECCV Workshop. Deadline: 2020.07.15.

Code

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

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]

GT generation

• Density Map Generation from Key Points [Matlab Code] [Python Code] [Fast Python Code] [Pytorch CUDA Code]

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Note that all unpublished arXiv papers are not included in the leaderboard of performance.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]
• CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability [paper]
• S2FPR: Crowd Counting via Self-Supervised Coarse to Fine Feature Pyramid Ranking [paper][code]
• 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]
• 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]
• Recurrent Distillation based Crowd Counting [paper]
• Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions [paper][code]
• 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]
• 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]
• Structured Inhomogeneous Density Map Learning for Crowd Counting [paper]

2024

Conference

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

2023

Conference

• [SAM] Training-free Object Counting with Prompts (WACV)[paper][code]
• [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]
• [STEERER] STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning (ICCV)[paper][code]
• [PET] Point-Query Quadtree for Crowd Counting, Localization, and More (ICCV)[paper][code]
• Striking a Balance: Unsupervised Cross-Domain Crowd Counting via Knowledge Diffusion (ACM MM)[paper]
• [AWCC-Net] Counting Crowds in Bad Weather (ICCV)[paper][code]
• [CU] Calibrating Uncertainty for Semi-Supervised Crowd Counting (ICCV)[paper][code]
• [DAOT] DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting (ACM MM)[paper]
• [ZSC] Zero-shot Object Counting (CVPR)[paper][code]
• [DDC] Diffuse-Denoise-Count: Accurate Crowd-Counting with Diffusion Models (CVPR)[paper][code]
• [IOCFormer] Indiscernible Object Counting in Underwater Scenes (CVPR)[paper][code]
• [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]
• [DGCC] Domain-general Crowd Counting in Unseen Scenarios (AAAI)[paper] [code]
• [SAFECount] Few-Shot Object Counting With Similarity-Aware Feature Enhancement (WACV)[paper] [code]
• [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]
• [CHS-Net] Cross-head Supervision for Crowd Counting with Noisy Annotations (ICASSP)[paper] [code]
• [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]
• [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]
• [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]
• [STGN] Spatial-Temporal Graph Network for Video Crowd Counting (T-CSVT) [paper] [code]
• [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]
• [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]
• [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]
• [LoViTCrowd] Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd Counting (BMVC) [paper] [code]
• [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]
• [CLTR] An End-to-End Transformer Model for Crowd Localization (ECCV) [paper] [code][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]
• [DACount] Semi-supervised-Crowd-Counting-via-Density-Agency (ACM MM) [paper][code]
• [ChfL] Crowd Counting in the Frequency Domain (CVPR) [paper][code]
• [GauNet] Rethinking Spatial Invariance of Convolutional Networks for Object Counting (CVPR) [paper][code]
• [DR.VIC] DR.VIC: Decomposition and Reasoning for Video Individual Counting (CVPR) [paper][code]
• [CDCC] Leveraging Self-Supervision for Cross-Domain Crowd Counting (CVPR) [paper][code]
• [MAN] Boosting Crowd Counting via Multifaceted Attention (CVPR) [paper][code]
• [BLA] Bi-level Alignment for Cross-Domain Crowd Counting (CVPR) [paper][code]
• [BMNet] Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting (CVPR)[paper][code]
• 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]
• [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]
• [TAFNet] TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting (ISCAS) [paper][code]
• [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]

Journal

• [PSGCNet] PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images (TGRS) [paper][code]
• [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]
• [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]
• [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] [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]
• [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]
• [UEPNet] Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting (ICCV) [paper][code]
• [SUA] Spatial Uncertainty-Aware Semi-Supervised Crowd Counting (ICCV) [paper][code]
• [DKPNet] Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV) [paper][code]
• [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]
• [C2MoT] Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting (ACM MM) [paper][code]
• [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]
• [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]
• [RGBT-CC] Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting (CVPR) [paper][code][Project]
• [EDIREC-Net] Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting (AAAI) [paper][code]
• [SASNet] To Choose or to Fuse? Scale Selection for Crowd Counting (AAAI) [paper][code]
• [UOT] Learning to Count via Unbalanced Optimal Transport (AAAI) [paper]
• [TopoCount] Localization in the Crowd with Topological Constraints (AAAI) [paper][code]
• [CFANet] Coarse- and Fine-grained Attention Network with Background-aware Loss for Crowd Density Map Estimation (WACV) [paper][code]
• [BSCC] Understanding the impact of mistakes on background regions in crowd counting (WACV) [paper]
• [CFOCNet] Class-agnostic Few-shot Object Counting (WACV) [paper][code]
• [SCALNet] Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization (ICMEW) [paper][code]
• [DSNet] Dense Scale Network for Crowd Counting (ICMR) [paper][unofficial code: PyTorch]
• [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]
• [EPF] Counting People by Estimating People Flows (TPAMI) [paper][code]
• [LA-Batch] Locality-Aware Crowd Counting (TPAMI) [paper]
• [AutoScale] AutoScale: Learning to Scale for Crowd Counting (IJCV) [paper] (extension of L2SM)[code]
• [DSACA] Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting (SPL) [paper] [code]
• [NLT] Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting (T-NNLS) [paper] [code]]
• [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]
• [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]
• [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]
• [DCANet] Towards Learning Multi-domain Crowd Counting (T-CSVT) [paper] [code]
• [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]
• [MNA] Modeling Noisy Annotations for Crowd Counting (NeurIPS) [paper]
• [SKT] Efficient Crowd Counting via Structured Knowledge Transfer (ACM MM(oral)) [paper][code]
• [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]
• [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]
• [LibraNet] Weighting Counts: Sequential Crowd Counting by Reinforcement Learning (ECCV) [paper][code]
• [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]
• [SRF-Net] Scale-Aware Rolling Fusion Network for Crowd Counting (ICME) [paper]
• [EDC] Learning Error-Driven Curriculum for Crowd Counting (ICPR) [paper][code]
• [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]
• [HSRNet] Crowd Counting via Hierarchical Scale Recalibration Network (ECAI) [paper]
• [DeepCount] Deep Density-aware Count Regressor (ECAI) [paper][code]
• [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]
• [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]
• [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]
• [KDMG] Kernel-based Density Map Generation for Dense Object Counting (T-PAMI) [paper][code]
• [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]
• [PWCU] Pixel-wise Crowd Understanding via Synthetic Data (IJCV) [paper]
• [CRNet] Crowd Counting via Cross-stage Refinement Networks (TIP) [paper][code]
• [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]
• [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]
• [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]
• [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]
• [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]
• [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]
• [BL] Bayesian Loss for Crowd Count Estimation with Point Supervision (ICCV(oral)) [paper][code]
• [PGCNet] Perspective-Guided Convolution Networks for Crowd Counting (ICCV) [paper][code]
• [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]
• [RRSP] Residual Regression with Semantic Prior for Crowd Counting (CVPR) [paper][code]
• [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]
• [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]
• [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]
• [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]
• [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]

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]
• [CSR] CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (CVPR) [paper] [code]
• [L2R] Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR) [paper] [code]
• [ACSCP] Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (CVPR) [paper] [unofficial code: PyTorch]
• [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]
• [D-ConvNet] Crowd Counting with Deep Negative Correlation Learning (CVPR) [paper] [code]
• [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]
• [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]

Journal

• [BSAD] Body Structure Aware Deep Crowd Counting (TIP) [paper]
• [NetVLAD] Multiscale Multitask Deep NetVLAD for Crowd Counting (TII) [paper] [code]
• [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]

2017

Conference

• [Switching CNN] Switching Convolutional Neural Network for Crowd Counting (CVPR) [paper] [code]
• [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]
• [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]
• [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]

2016

Conference

• [MCNN] Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (CVPR) [paper] [unofficial code: TensorFlow PyTorch]
• [Hydra-CNN] Towards perspective-free object counting with deep learning (ECCV) [paper] [code]
• [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]
• [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]
• [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