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[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.
[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 ]
[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.
[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 ]
Crowd Analysis, Crowd Localization , Video Surveillance , Dense/Small/Tiny Object Detection
Please refer to this page .
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):
Note that all unpublished arXiv papers are not included in the leaderboard of performance .
Focus for Free in Density-Based Counting [paper ]
Counting Crowds in Bad Weather [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 ]
Redesigning Multi-Scale Neural Network for Crowd Counting [paper ][code ]
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 ]
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP [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 ]
[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 ][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 ]
[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 ]
[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 ]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 ]
[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 ]
[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 ]
[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 ]
[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 ]
[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 ]
[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 ]
[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 ]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 ]
[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 ]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 ]
[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 ]
[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 ]
[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 ]
[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 ]
[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 ]
[FU 2015] Fast crowd density estimation with convolutional neural networks (Artificial Intelligence ) [paper ]
[Arteta 2014] Interactive Object Counting (ECCV ) [paper ]
[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 ]
[Chen 2012] Feature mining for localised crowd counting (BMVC ) [paper ]
[Rodriguez 2011] Density-aware person detection and tracking in crowds (ICCV ) [paper ]
[Lempitsky 2010] Learning To Count Objects in Images (NeurIPS ) [paper ]
[Chan 2008] Privacy preserving crowd monitoring: Counting people without people models or tracking (CVPR ) [paper ]
The section is being continually updated. Note that some values have superscript, which indicates their source.
Please refer to this page .
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
-
-
-
-
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
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 2013 CL 315
0.63
508
-
-
-
-
-
-
2016--CVPR
MCNN CL 277
0.55
426
0.006670
0.0223
0.5354
59.93%
63.50%
0.591
2017--AVSS
CMTL CL 252
0.54
514
0.005932
0.0244
0.5024
-
-
-
2017--CVPR
Switching CNN CL 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 -
-
-
-
-
-
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
[project]
[project]
[Project]
PyTorch]
